Description du projet :
PERSIST. Patients-centered care plan after Cancer treatments based on AI. Patient trajectory analytics based on Machine Learnig techniques.
Equipe de recherche au sein de la HES-SO:
, Schumacher Michael Ignaz
Partenaires académiques: -, University of Maribor
Partenaires professionnels: Gradiant
Durée du projet:
Statut: En cours
Rôle: Requérant(e) principal(e)
SemPryv: Automatic Semantization for Personalized Health Data. Machine Learning based methods for semantic annotation of personal data streams.
Partenaires professionnels: Pryv SA
Durée du projet:
01.04.2018 - 31.03.2020
Description du projet :
Equipe de recherche au sein de la HES-SO:
, Schumacher Michael Ignaz
, Eggenschwiler Stefan
, Gobet Pierre
, Fux Michael
Partenaires académiques: CTI; KTI
Durée du projet:
01.01.2016 - 31.12.2018
Montant global du projet: 445'489 CHF
VS - Institut Informatique; Nano-Tera.CH; SEFRI (anc. OFFT)
Description du projet :
Modern therapeutics must benefit from the development and large-scale implementation of convenient, user-friendly, miniaturized, integrated instruments enbling drug concentration monitoring and seamless pharmacokinetically guided dosage individualization.
Equipe de recherche au sein de la HES-SO:
Schumacher Michael Ignaz
, Calbimonte Jean-Paul
Durée du projet:
01.11.2013 - 31.10.2017
Montant global du projet: 451'335 CHF
VS - Institut Informatique; Nano-Tera.CH; SEFRI (anc. OFFT); VS - Institut Informatique
Description du projet :
Diabetes type 1 is an autoimmune disease that affects the insulin-producing beta cells of the islets of Langerhans in the pancreas leading to insulin deficiency. Once a patient is affected by diabetes type 1, the only possible treatment is insulin shots several time a day to keep the insulin levels under control and keep the risk of hypoglycaemia low. In particular, the occurrence of hypoglycaemia in diabetes type 1 is quite frequent in intensively treated patients. As reported in , intensively treated patients with type 1 diabetes can experience even 10 episodes of symptomatic hypoglycaemia each week and a severe temporarily disabling hypoglycaemia up to once per year. Furthermore, 2-4% of the deaths in type 1 diabetes are caused by severe hypoglycaemic attacks
Equipe de recherche au sein de la HES-SO:
Schumacher Michael Ignaz
Durée du projet:
01.11.2013 - 31.10.2016
CTI; Biovotion AG
Description du projet :
COMPASS will define and create a Personal Health System to monitor the physicological parameters of patients affected by Chronic Obstructive Pulmonary Disease and assiciated chronic illnesses and predict the physiological state as a support to the decisions taken by medical doctors.
Partenaires académiques: VS - Institut Informatique; Schumacher Michael Ignaz, VS - Institut Informatique
Durée du projet:
01.04.2014 - 30.06.2016
Montant global du projet: 497'893 CHF
Description du projet :
The context of this project is the Personal Health Systems (PHS) used in the field of ubiquitous healthcare. The particular goals to achieve are the development of a multiagent platform (MAP) integrated with the Android OS, with the aim of facilitating the development of mobile applications for monitoring chronic illnesses; and to study how to apply concepts from Distributed Event Based Systems (DEBS) into PHS with the aim of helping patients to connect with other patients. With this project we pretend to illustrate how we can improve the patient's quality of life using MAP and DEBS technologies. Such an agent-based PHS for Android would be a clear step forward in the state-of-the-art, and would allow deploying this platform for future eHealth projects. This project will also setup a strong collaboration with the Universitat Politècnica de Catalunya, BarcelonaTech
Durée du projet:
01.10.2014 - 31.03.2016
Description du projet :
Grant Agreement: 287841
COMMODITY12 aims to design, build, and validate an intelligent system for the analysis of multi-parametric medical data. It will uptake the existing cutting-edge technologies and extend these technologies by combining state-of-the-art networks, software interoperation, and artificial intelligence techniques in order to realize the concept of translational medicine by means of a Personal Health System. Moreover, the COMMODITY12 system will build a new level in patient empowerment, providing the tools for self-management support. Indirectly, this system will also help wider implementation of Personal Health Systems, reinforcing leadership and innovation capability of the European industry in that area.
Partenaires académiques: VS - Institut Informatique
Durée du projet:
01.10.2011 - 30.06.2015
Montant global du projet: 632'026 CHF
Description du projet :
This research project defines a coordination framework that combines multiple clinical guidelines relating to patients diagnosed with the metabolic syndrome. Patients and caregivers are connected within a decentralized
network that enables caregivers to exchange health records and to adhere to the clinical guidelines.
The first goal of this project is the definition of a semantic model for specifying the clinical rules that reason with heterogenous patient records. The second goal is to formalize and prototype this model by combining it with a declarative language that can identify intervention plans within the co-morbidities of the metabolic syndrome. Finally, the third goal is to evaluate the approach within a small feasibility study. The expected results are a general framework for reasoning with multiple clinical guidelines, and that extends the current ability that caregivers have to exchange focused and specialized patients' health data. From the eHealth perspective, the benefit stands in the fact that we can assist caregivers to perform timely intervension measures that are shown to benefit the overall patient's health and lower the overall healthcare costs.
Durée du projet:
01.05.2014 - 30.06.2015
Montant global du projet: 49'975 CHF
Davide Calvaresi, Stefan Eggenschwiler, Yazan Mualla, Michael Schumacher, Jean-Paul Calbimonte
Journal of ambient intelligence and humanized computing,
2023, vol. 14, pp. 11207–11226
Lien vers la publication
In the last decade, conversational agents have been developed and adopted in several application domains, including education, healthcare, finance, and tourism. Nevertheless, chatbots still need to address several limitations and challenges, especially regarding personalization, limited knowledge-sharing capabilities, multi-domain campaign support, real-time monitoring, or integration of chatbot communities. To cope with these limitations, many approaches based on multi-agent systems models and technologies have been proposed in the literature, opening new research directions in this context. To better understand the current panorama of the different chatbot technology solutions employing agent-based methods, this Systematic Literature Review investigates the different application domains, end-users, requirements, objectives, technology readiness levels, designs, strengths, limitations, and future challenges of the solutions found in this scope. The results of this review are intended to provide researchers, software engineers, and innovators with a complete overview of the current state of the art and a discussion of the open challenges.
Emanuele Gagliardi, Gabriele Bernardini, Enrico Quagliarini, Michael Schumacher, Davide Calvaresi
July 2023, vol. 163, 106141
Physical evacuation drills are pre-planned activities to train building occupants in facing emergencies and evaluate safety performances. Nowadays, technologies including Virtual Reality (VR) and Immersive Virtual Reality (IVR) are shifting from the physical to the virtual paradigm. AR enables just to extend real-world environment, while VR and IVR allow to (re)create and manipulate digital environments. VR and IVR simulation systems have been observed to guarantee higher involvement and long-term information retention — leveraging more attractive experiences and psychological arousal. However, efforts should be provided to improve end-user training while assessing occupants’ behaviors and the effectiveness of the emergency plan. This paper proposes a systematic literature review of VR and IVR evacuation solutions. To support and guide such effort, we formulated thirteen structured research questions investigating scenarios, recipients, requirements, objectives, methods, and technologies. The results mainly show that VR and IVR drills almost entirely tackle a single hazard, considers occupants as sole system recipients, and lack systems formalization. Among the most relevant outcomes, the paper analyzes the need for enhancing the modeling of emergency systems (e.g., signage, alarms), user inclusiveness (i.e., impaired individuals), devices, non-player characters, and additional effects (e.g., heat reproduction, sounds, and smells). These measures can improve the level of realism experienced by the user of IVR simulators and pave the way to more reliable outcomes.
Gaetano Manzo, Yves Pannatier, Patrick Duflot, Philippe Kolh, Marcela Chavez, Valérie Bleret, Davide Calvaresi, Oscar Jimenez-del-Toro, Michael Schumacher, Jean-Paul Calbimonte
Computer methods and programs in biomedicine,
Avril 2023, vol. 231
Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors’ trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.
Jean-Paul Calbimonte, Orfeas Aidonopoulos, Fabien Dubosson, Benjamin Pocklington, Ilia Kebets, Pierre-Mikael Legris, Michael Schumacher
Journal of web semantics,
Avril 2023, vol. 76
Personalized healthcare is nowadays driven by the increasing volumes of patient data, observed and produced continuously thanks to medical devices, mobile sensors, patient-reported outcomes, among other data sources. This data is made available as streams, due to their dynamic nature, which represents an important challenge for processing, querying and interpreting the incoming information. In addition, the sensitive nature of healthcare data poses significant restrictions regarding privacy, which has led to the emergence of decentralized personal data management systems. Data semantics play a key role in order to enable both decentralization and integration of personal health data, as they introduce the capability to represent knowledge and information using ontologies and semantic vocabularies. In this paper we describe the SemPryv system, which provides the means to manage personal health data streams enriched with semantic information. SemPryv is designed as a decentralized system, so that users have the possibility of hosting their personal data at different sites, while keeping control of access rights. The semantization of data in SemPryv is implemented through different strategies, ranging from rule-based annotation to machine learning-based suggestions, fed from third-party specialized healthcare metadata providers. The system has been made available as Open Source, and is integrated as part of the Pryv.io platform used and commercialized in the healthcare and personal data management industry
Victor Contreras, Niccolo Marini, Lora Fanda, Gaetano Manzo, Yazan Mualla, Jean-Paul Calbimonte, Michael Schumacher, Davide Calvaresi
2022, vol. 11, no 24, p. 4171
Background: Despite the advancement in eXplainable Artificial Intelligence, the expla nations provided by model-agnostic predictors still call for improvements (i.e., lack of accurate descriptions of predictors’ behaviors). Contribution: We present a tool for Deep Explanations and Rule Extraction (DEXiRE) to approximate rules for Deep Learning models with any number of hid den layers. Methodology: DEXiRE proposes the binarization of neural networks to induce Boolean functions in the hidden layers, generating as many intermediate rule sets. A rule set is inducted between the first hidden layer and the input layer. Finally, the complete rule set is obtained using inverse substitution on intermediate rule sets and first-layer rules. Statistical tests and satisfiability algorithms reduce the final rule set’s size and complexity (filtering redundant, inconsistent, and non-frequent rules). DEXiRE has been tested in binary and multiclass classifications with six datasets having different structures and models. Results: The performance is consistent (in terms of accuracy, fidelity, and rule length) with respect to the state-of-the-art rule extractors (i.e., ECLAIRE). Moreover, compared with ECLAIRE, DEXiRE has generated shorter rules (i.e., up to 74% fewer terms) and has shortened the execution time (improving up to 197% in the best-case scenario). Conclusions: DEXiRE can be applied for binary and multiclass classification of deep learning predictors with any number of hidden layers. Moreover, DEXiRE can identify the activation pattern per class and use it to reduce the search space for rule extractors (pruning irrelevant/redundant neurons)—shorter rules and execution times with respect to ECLAIRE.
Davide Calvaresi, Rachele Carli, Jean-Gabriel Piguet, Victor H. Contreras, Gloria Luzzani, Amro Najjar, Jean-Paul Calbimonte, Michael Schumacher
AI and Ethics,
Choices and preferences of individuals are nowadays increasingly infuenced by countless inputs and recommendations provided by artifcial intelligence-based systems. The accuracy of recommender systems (RS) has achieved remarkable results in several domains, from infotainment to marketing and lifestyle. However, in sensitive use-cases, such as nutrition, there is a need for more complex dynamics and responsibilities beyond conventional RS frameworks. On one hand, virtual coaching systems (VCS) are intended to support and educate the users about food, integrating additional dimensions w.r.t. the conventional RS (i.e., leveraging persuasion techniques, argumentation, informative systems, and recommendation paradigms) and show promising results. On the other hand, as of today, VCS raise unexplored ethical and legal concerns. This paper discusses the need for a clear understanding of the ethical/legal-technological entanglements, formalizing 21 ethical and ten legal challenges and the related mitigation strategies. Moreover, it elaborates on nutrition sustainability as a further nutrition virtual coaches dimension for a better society.
Emmanuel Fragnière, Jean-Michel Sahut, Lubica Hikkerova, Michael Schumacher, Sandra Grèzes, Randolf Ramseyer
Journal of innovation economics management,
2022, no. 37, pp. 65-90
Blockchain is often presented in the tourism industry as being a technology in a global approach that will enable the sector to make its digital transformation and bring a whole series of advantages, both financial and logistical. The reality is different, however. Indeed, research on Blockchain tends to focus on essentially technical aspects and takes little account of the customer experience in the sector in which it will be integrated. The aim of this exploratory research, based on 18 semi-directive interviews, is to understand the sociological obstacles to the adoption of Blockchain by tourism professionals in Switzerland. Our generalized findings are presented in the form of four research proposals that argue that, without the intervention of the State, it is hard to see how such disruptive innovation can radically change the highly fragmented tourism sector.
Arnaud Chiolero, Jean-Paul Calbimonte, Gaetano Manzo, Bruno Alves, Michael Schumacher, Samuel Gaillard, Philippe Schaller, Valérie Santschi
Revue médicale suisse,
2021, vol. 17, no. 760, pp. 2056-2059
Gaetano Manzo, Davide Calvaresi, Oscar Alfonso Jiménez del Toro, Jean-Paul Calbimonte, Michael Schumacher
Journal of medical systems,
2021, vol. 45, article no. 109, pp. 1-10
In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient’s high-risk markers, and support treatment decisions.
Davide Calvaresi, Ahmed Ibrahim, Jean-Paul Calbimonte, Emmanuel Fragnière, Roland Schegg, Michael Schumacher
Journal of tourism futures,
To be published
Purpose : The tourism and hospitality sectors are experiencing radical innovation boosted by the advancements in Information and Communication Technologies. Increasingly sophisticated chatbots are introducing novel approaches, re-shaping the dynamics among tourists and service providers, and fostering a remarkable behavioral change in the overall sector. Therefore, the objective of this paper is two-folded: (1) to highlight the academic and industrial standing points with respect to the current chatbots designed/deployed in the tourism sector and (2) to develop a proof-of-concept embodying the most prominent opportunities in the tourism sector. Design/methodology/approach : This work elaborates on the outcomes of a Systematic Literature Review (SLR) and a Focus Group (FG) composed of experts from the tourism industry. Moreover, it presents a proof-of-concept relying on the outcomes obtained from both SLR and FG. Eventually, the proof-of-concept has been tested with experts and practitioners of the tourism sector. Findings : Among the findings elicited by this paper, we can mention the quick evolution of chatbot-based solutions, the need for continuous investments, upskilling, system innovation to tackle the eTourism challenges and the shift toward new dimensions (i.e. tourist-to-tourist-to-chatbot and personalized multi-stakeholder systems). In particular, we focus on the need for chatbot-based activity and thematic aggregation for next-generation tourists and service providers. Originality/value : Both academic- and industrial-centered findings have been structured and discussed to foster the practitioners' future research. Moreover, the proof-of-concept presented in the paper is the first of its kind, which raised considerable interest from both technical and business-planning perspectives.
Davide Calvaresi, Jean-Paul Calbimonte, Enrico Siboni, Stefan Eggenschwiler, Gaetano Manzo, Roger Hilfiker, Michael Schumacher
2021, vol. 10, no. 6, pp. article no. 666, pp. 1-30
Context. Asynchronous messaging is increasingly used to support human–machine interactions, generally implemented through chatbots. Such virtual entities assist the users in activities of different kinds (e.g., work, leisure, and health-related) and are becoming ingrained into humans’ habits due to factors including (i) the availability of mobile devices such as smartphones and tablets, (ii) the increasingly engaging nature of chatbot interactions, (iii) the release of dedicated APIs from messaging platforms, and (iv) increasingly complex AI-based mechanisms to power the bots’ behaviors. Nevertheless, most of the modern chatbots rely on state machines (implementing conversational rules) and one-fits-all approaches, neglecting personalization, data-stream privacy management, multi-topic management/interconnection, and multimodal interactions. Objective. This work addresses the challenges above through an agent-based framework for chatbot development named EREBOTS. Methods. The foundations of the framework are based on the implementation of (i) multi-front-end connectors and interfaces (i.e., Telegram, dedicated App, and web interface), (ii) enabling the configuration of multi-scenario behaviors (i.e., preventive physical conditioning, smoking cessation, and support for breast-cancer survivors), (iii) online learning, (iv) personalized conversations and recommendations (i.e., mood boost, anti-craving persuasion, and balance-preserving physical exercises), and (v) responsive multi-device monitoring interface (i.e., doctor and admin). Results. EREBOTS has been tested in the context of physical balance preservation in social confinement times (due to the ongoing pandemic). Thirteen individuals characterized by diverse age, gender, and country distribution have actively participated in the experimentation, reporting advancements in the physical balance and overall satisfaction of the interaction and exercises’ variety they have been proposed.
Davide Calvaresi, Yashin Dicente Cid, Mauro Marinoni, Aldo Franco Dragoni, Amro Najjar, Michael Schumacher
Autonomous agents and multi-agent systems,
2021, vol. 35, article no. 12, pp. 1-37
Since its dawn as a discipline, Artificial Intelligence (AI) has focused on mimicking the human mental processes. As AI applications matured, the interest for employing them into real-world complex systems (i.e., coupling AI with Cyber-Physical Systems—CPS) kept increasing. In the last decades, the multi-agent systems (MAS) paradigm has been among the most relevant approaches fostering the development of intelligent systems. In numerous scenarios, MAS boosted distributed autonomous reasoning and behaviors. However, many real-world applications (e.g., CPS) demand the respect of strict timing constraints. Unfortunately, current AI/MAS theories and applications only reason “about time” and are incapable of acting “in time” guaranteeing any timing predictability. This paper analyzes the MAS compliance with strict timing constraints (real-time compliance)—crucial for safety-critical applications such as healthcare, industry 4.0, and automotive. Moreover, it elicits the main reasons for the lack of real-time satisfiability in MAS (originated from current theories, standards, and implementations). In particular, traditional internal agent schedulers (general-purpose-like), communication middlewares, and negotiation protocols have been identified as co-factors inhibiting real-time compliance. To pave the road towards reliable and predictable MAS, this paper postulates a formal definition and mathematical model of real-time multi-agent systems (RT-MAS). Furthermore, this paper presents the results obtained by testing the dynamics characterizing the RT-MAS model within the simulator MAXIM-GPRT. Thus, it has been possible to analyze the deadline miss ratio between the algorithms employed in the most popular frameworks and the proposed ones. Finally, discussing the obtained results, the ongoing and future steps are outlined.
Giuseppe Albanese, Jean-Paul Calbimonte, Michael Schumacher, Davide Calvaresi
Journal of ambient intelligence and humanized computing,
2020, vol. 11, no. 11, pp. 4909–4926
Clinical trials (CTs) are essential for the advancement of medical research, paving the way for the development and adoption of new treatments, and contributing to the evolution of healthcare. An essential factor for the success of a CT is the appropriate management of its participants and their personal data. According to the current regulations, collecting and using personal data from participants must comply with rigorous standards. Therefore, healthcare institutes need to obtain freely given, specific, informed, and unambiguous consent before being able to collect the data. Some of the major limitations of the current technological solutions are the lack of control over the granularity of consent grants, as well as the difficulty of handling dynamic changes of consent over time. In this paper, we present SCoDES, an approach for trusted and decentralized management of dynamic consent in clinical trials, based on blockchain technology (BCT). The usage of blockchain provides a set of features that allow maintaining consent information with trust guarantees while avoiding the need for a dedicated or centralized third trusted party. We provide a full implementation of SCoDES, made available as a self-contained infrastructure, with the possibility to interact with external services, and using hyperledger as a blockchain framework.
Davide Calvaresi, Michael Schumacher, Jean-Paul Calbimonte
Journal of medical systems,
2020, vol. 44, no. 9, article 158
Patients are often required to follow a medical treatment after discharge, e.g., for a chronic condition, rehabilitation after surgery, or for cancer survivor therapies. The need to adapt to new lifestyles, medication, and treatment routines, can produce an individual burden to the patient, who is often at home without the full support of healthcare professionals. Although technological solutions –in the form of mobile apps and wearables– have been proposed to mitigate these issues, it is essential to consider individual characteristics, preferences, and the context of a patient in order to offer personalized and effective support. The specific events and circumstances linked to an individual profile can be abstracted as a patient trajectory, which can contribute to a better understanding of the patient, her needs, and the most appropriate personalized support. Although patient trajectories have been studied for different illnesses and conditions, it remains challenging to effectively use them as the basis for data analytics methodologies in decentralized eHealth systems. In this work, we present a novel approach based on the multi-agent paradigm, considering patient trajectories as the cornerstone of a methodology for modelling eHealth support systems. In this design, semantic representations of individual treatment pathways are used in order to exchange patient-relevant information, potentially fed to AI systems for prediction and classification tasks. This paper describes the major challenges in this scope, as well as the design principles of the proposed agent-based architecture, including an example of its use through a case scenario for cancer survivors support.
Neil Vaughan, Eloisa Vargiu, Stefano Mariani, Sara Montagna, Michael Schumacher
Journal of medical systems,
2020, vol. 44, no. 8, article no. 138, pp. 1-2
Alevtina Dubovitskaya, Zhigang Xu, Furqan Baig, Rohit Shukla, Pratik Sushil Zambani, Arun Swaminathan, Majid Jahangir, Khadija Chowdhry, Rahul Lachhani, Nitesh Idnani, Michael Schumacher, Karl Aberer, Scott D. Stoller, Samuel Ryu, Fusheng Wang
Journal of medical internet research,
2020, vol. 22, no. 8, pp. 1-15
Background: With increased specialization of health care services and high levels of patient mobility, accessing health care services across multiple hospitals or clinics has become very common for diagnosis and treatment, particularly for patients with chronic diseases such as cancer. With informed knowledge of a patient’s history, physicians can make prompt clinical decisions for smarter, safer, and more efficient care. However, due to the privacy and high sensitivity of electronic health records (EHR), most EHR data sharing still happens through fax or mail due to the lack of systematic infrastructure support for secure, trustable health data sharing, which can also cause major delays in patient care.
Objective: Our goal was to develop a system that will facilitate secure, trustable management, sharing, and aggregation of EHR data. Our patient-centric system allows patients to manage their own health records across multiple hospitals. The system will ensure patient privacy protection and guarantee security with respect to the requirements for health care data management, including the access control policy specified by the patient.
Methods: We propose a permissioned blockchain-based system for EHR data sharing and integration. Each hospital will provide a blockchain node integrated with its own EHR system to form the blockchain network. A web-based interface will be used for patients and doctors to initiate EHR sharing transactions. We take a hybrid data management approach, where only management metadata will be stored on the chain. Actual EHR data, on the other hand, will be encrypted and stored off-chain in Health Insurance Portability and Accountability Act–compliant cloud-based storage. The system uses public key infrastructure–based asymmetric encryption and digital signatures to secure shared EHR data.
Results: In collaboration with Stony Brook University Hospital, we developed ACTION-EHR, a system for patient-centric, blockchain-based EHR data sharing and management for patient care, in particular radiation treatment for cancer. The prototype was built on Hyperledger Fabric, an open-source, permissioned blockchain framework. Data sharing transactions were implemented using chaincode and exposed as representational state transfer application programming interfaces used for the web portal for patients and users. The HL7 Fast healthcare Interoperability Resources standard was adopted to represent shared EHR data, making it easy to interface with hospital EHR systems and integrate a patient’s EHR data. We tested the system in a distributed
environment at Stony Brook University using deidentified patient data.
Conclusions: We studied and developed the critical technology components to enable patient-centric, blockchain-based EHR sharing to support cancer care. The prototype demonstrated the feasibility of our approach as well as some of the major challenges. The next step will be a pilot study with health care providers in both the United States and Switzerland. Our work provides an exemplar testbed to build next-generation EHR sharing infrastructures.
Davide Calvaresi, Jean-Paul Calbimonte, Alevtina Dubovitskaya, Valerio Mattioli, Jean-Gabriel Piguet, Michael Schumacher
2019, vol. 10, no. 12, article 363
The agent based approach is a well established methodology to model distributed intelligent systems. Multi-Agent Systems (MAS) are increasingly employed in applications dealing with safety and information critical tasks (e.g., in eHealth, financial, and energy domains). Therefore, transparency and the trustworthiness of the agents and their behaviors must be enforced. For example, employing reputation based mechanisms can promote the development of trust. Nevertheless, besides recent early stage studies, the existing methods and systems are still unable to guarantee the desired accountability and transparency adequately. In line with the recent trends, we advocate that combining blockchain technology (BCT) and MAS can achieve the distribution of the trust, removing the need for trusted third parties (TTP), potential single points of failure. This paper elaborates on the notions of trust, BCT, MAS, and their integration. Furthermore, to attain a trusted environment, this manuscript details the design and implementation of a system reconciling MAS (based on the Java Agent DEvelopment Framework (JADE)) and BTC (based on Hyperledger Fabric). In particular, the agents’ interactions, computation, tracking the reputation, and possible policies for disagreement-management are implemented via smart contracts and stored on an immutable distributed ledger. The results obtained by the presented system and similar solutions are also discussed. Finally, ethical implications (i.e., opportunities and challenges) are elaborated before concluding the paper.
Jean-Paul Calbimonte, Davide Calvaresi, Fabien Dubosson, Michael Schumacher
Dans Demazeau, Yves, Advances in practical applications of survivable agents and multi-agent systems : the PAAMS collection
(pp. 16-28). 2019,
Cham : Springer
Lien vers la publication
Health support programs play a vital role in public health and prevention strategies at local and national levels, for issues such as smoking cessation, physical rehabilitation, nutrition, or to regain mobility. A key success factor in these topics is related to the appropriate use of behavior change techniques, as well as tailored recommendations for users/patients, adapted to their goals and the continuous monitoring of their progress. Social networks interactions and the use of multi-agent technologies can further improve the effectiveness of these programs, especially through personalization and profiling of users and patients. In this paper we propose an agent-based model for supporting behavior change in eHealth programs. Moreover, we identify the main challenges in this area, especially regarding profile and domain modeling profiles for healthcare behavioral programs, where the definition of goals, expectations and argumentation play a key role in the success of a intervention.
Davide Calvaresi, Ekaterina Voronova, Jean-Paul Calbimonte, Valerio Mattioli, Michael Schumacher
Dans De La Prieta. Fernando, et al., Highlights of practical applications of survivable agents and multi-agent systems. The PAAMS collection: International Workshops of PAAMS 2019, Ávila, Spain, June 26–28, 2019, Proceedings
(12 p.). 2019,
Cham : Springer
The dynamic nature of startups is linked to both high risks
in investments as well as potentially important financial benefits. A key
aspect to manage interactions among investors, experts, and startups, is
the establishment of trust guarantees. This paper presents the formalization
and implementation of a system enforcing trust in the startup assessment
domain. To do so, an existing architecture has been extended,
incorporating a multi-agent community and related interactions via private
blockchain technology. The developed system enables a trust-based
community, immutably storing, tracking, and monitoring the agents’ interactions
Davide Calvaresi, Mauro Marinoni, Aldo Franco Dragoni, Roger Hilfiker, Michael Schumacher
Artificial intelligence in medicine,
2019, vol. 96, pp. 217-231
Telerehabilitation in older adults is most needed in the patient environments, rather than in formal ambulatories or hospitals. Supporting such practices brings significant advantages to patients, their family, formal and informal caregivers, clinicians, and researchers.
This paper presents a focus group with experts in physiotherapy and telerehabilitation, debating on the requirements, current techniques and technologies developed to facilitate and enhance the effectiveness of telerehabilitation, and the still open challenges. Particular emphasis is given to (i) the body-parts requiring the most rehabilitation, (ii) the typical environments, initial causes, and general conditions, (iii) the values and parameters to be observed, (iv) common errors and limitations of current practices and technological solutions, and (v) the envisioned and desired technological support. Consequently, it has been performed a systematic review of the state of the art, investigating what types of systems and support currently cope with telerehabilitation practices and possible matches with the outcomes of the focus group. Technological solutions based on video analysis, wearable devices, robotic support, distributed sensing, and gamified telerehabilitation are examined. Particular emphasis is given to solutions implementing agent-based approaches, analyzing and discussing strength, limitations, and future challenges. By doing so, it has been possible to relate functional requirements expressed by professional physiotherapists and researchers, with the need for extending multi-agent systems (MAS) peculiarities at the sensing level in wearable solutions establishing new research challenges. In particular, to be employed in safety-critical cyber-physical scenarios with user-sensor and sensor-sensor interactions, MAS are requested to handle timing constraints, scarcity of resources and new communication means, crucial to providing real-time feedback and coaching. Therefore, MAS pillars such as the negotiation protocol and the agent's internal scheduler have been investigated, proposing solutions to achieve the aforementioned real-time compliance.
Davide Calvaresi, Maxine Leis, Alevtina Dubovitskaya, Roland Schegg, Michael Schumacher
Dans Neidhardt, Julia, Pesonen, Juho, Information and Communication Technologies in Tourism 2019 : proceedings of the International Conference in Nicosia, Cyprus, January 30–February 1, 2019
(12 p.). 2018,
Cham : Springer
Trust-free and trust-regulated systems based on blockchain technology (BCT) are currently experiencing the maximum hype and promise to revolutionise entire domains. Tourism products (intangible services) are highly dependent on trust and reputation management that is traditionally centralised and delegated to “expected” reliable third-parties (e.g., TripAdvisor). Although BCT has only recently started approaching the tourism industry and being employed in real-world applications, the scientific community has already been extensively exploring the promises of BCT. Therefore, there is an impending need for organising and understanding current knowledge and formalise societal, scientific, and technological challenges of applying BCT in the tourism industry. This paper moves the first step, presenting a systematic scientific literature review of studies involving BCT for tourism purposes. Providing a comprehensive overview, actors, assumptions, requirements, strengths, and limitations characterising the state of the art are analysed. Finally, advantages and future challenges of applying BCT in the tourism area are discussed.
Davide Calvaresi, Alevtina Dubovitskaya, Jean-Paul Calbimonte, Kuldar Taveter, Michael Schumacher
Dans An, Bo, Bajo, Javier, Demazeau, Yves, Fernández-Caballero, Antonio, Advances in practical applications of agents, multi-agent systems, and complexity : the PAAMS collection : 16th International Conference, PAAMS 2018, Toledo, Spain, June 20–22, 2018, Proceedings
(Pp. 110-126). 2018,
Cham : Springer
Multi-Agent Systems (MAS) technology is widely used for the development of intelligent distributed systems that manage sensitive data (e.g., ambient assisted living, healthcare, energy trading). To foster accountability and trusted interactions, recent trends advocate the use of blockchain technologies (BCT) for MAS. Although most of these approaches have only started exploring the topic, there is an impending need for establishing a research road-map, as well as identifying scientific and technological challenges in this scope. As a first necessary step towards this goal, this paper presents a systematic literature review of studies involving MAS and BCT as reconciling solutions. Aiming at providing a comprehensive overview of their application domains, we analyze motivations, assumptions, requirements, strengths, and limitations presented in the current state of the art. Moreover, discussing the future challenges, we introduce our vision on how MAS and BCT could be combined in different application scenarios.
Giuseppe Albanese, Davide Calvaresi, Paolo Sernani, Fabien Dubosson, Aldo Franco Dragoni, Michael Schumacher
Dans An, Bo, Bajo, Javier, Demazeau, Yves, Fernández-Caballero, Antonio, Advances in practical applications of agents, multi-agent systems, and complexity : The PAAMS collection : 16th International Conference, PAAMS 2018, Toledo, Spain, June 20–22, 2018, Proceedings
(Pp. 291-295). 2018,
Cham : Springer
In safety-critical scenarios, the compliance with strict-timing constraints is mandatory. This demo presents a simulator named MAXIM-GPRT enabling the analysis of the behaviors produced by Multi-Agent Systems (MAS) composed of both General-Purpose (GP) and Real-time (RT) algorithms. Therefore, MAXIM-GPRT is crucial to prove that current MAS cannot provide timing guarantees, nor guarantee correct behaviors in the worst case scenario. However, adopting and adapting models and algorithms from RT systems, such a compliance, can be achieved.
Davide Calvaresi, Kevin Appoggetti, Luca Lustrissimini, Mauro Marinori, Paolo Sernani, Aldo Franco Dragoni, Michael Schumacher
Dans Rocha, Ana Paula, Proceedings of the 10th International Conference on Agents and Artificial Intelligence
(pp. 224-235). 2018,
Setubal : SciTePress
Cyber Physical Systems (CPS) require a multitude of components interacting among themselves and with the users to perform automatic actions, usually under unpredictable or uncertain conditions. Multi-Agent Systems (MAS) have emerged over the years as one of the major technological paradigms regulating interactions and negotiations among autonomous entities running under heterogeneous conditions. As such, MAS have the potential to support CPS in implementing a highly reconfigurable distributed thinking. However, some gaps are still present between MAS’ features and the strict requirements of CPS. The most relevant is the lack of reliability, which is mainly due to specific features characterizing negotiation protocols. This paper presents a systematic literature review of MAS negotiation protocols aiming at providing a comprehensive overview of their strengths and limitations, examining both the assumptions and requirements set during their development. While this work confirms the potential of MAS in regulating the interactions among CPS components, the findings also highlight the absence of real-time compliance in current negotiation protocols. Strongly characterizing CPS, the capability to face strict time constraints could bridge the gap between MAS and CPS.
Jean-Paul Calbimonte, Fabien Dubosson, Roger Hilfiker, Alexandre Cotting, Michael Schumacher
The Semantic Web – ISWC 2017 : 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II
(pp. 38-47). 2017,
Cham : Springer
Electronic Data Capture (EDC) software solutions are progressively being adopted for conducting clinical trials and studies, carried out by biomedi-cal, pharmaceutical and health-care research teams. In this paper we present the MedRed Ontology, whose goal is to represent the metadata of these studies, using well-established standards, and reusing related vocabularies to describe essential aspects, such as validation rules, composability, or provenance. The paper de-scribes the design principles behind the ontology and how it relates to existing models and formats used in the industry. We also reuse well-known vocabularies and W3C recommendations. Furthermore, we have validated the ontology with ex-isting clinical studies in the context of the MedRed project, as well as a collection of metadata of well-known studies. Finally, we have made the ontology available publicly following best practices and vocabulary sharing guidelines.
Alevtina Dubovitskaya, Zhigang Xu, Samuel Ryu, Michael Schumacher, Fusheng Wang
Swiss medical informatics,
Electronic medical records (EMRs) are critical, highly sen-sitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trustability, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system by using blockchain. We present our perspectives on blockchain-based health care data management. We implemented a framework for managing and sharing EMR data on cancer patient care that ensures privacy, security, availability, and fine-grained access control over EMR data.
V. Urovi, Oscar Alfonso Jiménez del Toro, Fabien Dubosson, A. Ruiz Torres, Michael Schumacher
Computers in biology and medicine,
2017, vol. 81, pp. 24-31
This paper describes a novel temporal logic-based framework for reasoning with continuous data collected from wearable sensors. The work is motivated by the Metabolic Syndrome, a cluster of conditions which are linked to obesity and unhealthy lifestyle. We assume that, by interpreting the physiological parameters of continuous monitoring, we can identify which patients have a higher risk of Metabolic Syndrome. We define temporal patterns for reasoning with continuous data and specify the coordination mechanisms for combining different sets of clinical guidelines that relate to this condition. The proposed solution is tested with data provided by twenty subjects, which used sensors for four days of continuous monitoring. The results are compared to the gold standard. The novelty of the framework stands in extending a temporal logic formalism, namely the Event Calculus, with temporal patterns. These patterns are helpful to specify the rules for reasoning with continuous data and in combining new knowledge into one consistent outcome that is tailored to the patient's profile. The overall approach opens new possibilities for delivering patient-tailored interventions and educational material before the patients present the symptoms of the disease.
Fabien Dubosson, Roger Schaer, Roland Savioz, Michael Schumacher
Swiss medical informatics,
2017, vol. 33, 10 p.
Research question: A social network programme called J’arrête de fumer was set up in 2016 in the six French-speaking cantons of Switzerland. It consists of Facebook groups where people agree on a date to quit smoking. A peak of relapse appears during the first three weeks of the programme. This research aims to explore the feasibility of building a Chatbot to help people to get over this peak in future iterations of the programme. Methods: It has been shown that the urge to smoke may be one of the reasons for relapses. Being able to distract users from the idea of smoking during these phases would help them to get through these three first weeks. Due to the large number of participants, a human intervention within the craving time frame is difficult to achieve, but such a constraint would be easier to overcome with ChatBots. Results: A ChatBot for the Telegram platform has been developed. It offers five different modules to overtake the time frame where the urge to smoke is greatest. Some of these modules, such as motivating comments and factual information, are already well used, but some others are less widely explored, like helping scientific research by classifying images or putting people in touch with each other as another form of distraction. Conclusion: ChatBots offer interesting opportunities for helping smoking cessation communities, as they would help participants during craving time frames and would be able to handle the large number of participants.
Jean-Paul Calbimonte, Fabien Dubosson, Roger Hil?ker, Alexandre Cotting, Michael Schumacher
Swiss Medical Informatics,
2017, vol. 33, pp. 1-7
Research in the health-care domain requires the collection of important and exhaustive datasets, in order to validate a scientific hypothesis, or to assess the effectiveness of a treatment, technology, medicine, or procedure. The data acquisition phase for this type of work requires an often under-estimated amount of time and effort, while needing to keep high quality standards for the entire process. Many of the tasks associated with data acquisition are often carried out manually, resulting in error-prone procedures, hand-transcription, inaccuracy, and time delays to produce a resulting usable dataset. This paper presents MedRed (Medical Research Data Acquisition Platform ), a platform and a service designed to facilitate the data acquisition process for researchers in the health-care do-main, using the REDCap software for data capture. This service is available in a first stage, for all scientists of the HES-SO (University of Applied Sciences and Arts Western Switzerland) schools in Switzerland, and partially supported by the SwissUniversities CUS-P2 program.
Sandrine Ding, Michael Schumacher
2016, vol. 16, no. 4, p. 589
Diabetic individuals need to tightly control their blood glucose concentration. Several methods have been developed for this purpose, such as the finger-prick or continuous glucose monitoring systems (CGMs). However, these methods present the disadvantage of being invasive. Moreover, CGMs have limited accuracy, notably to detect hypoglycemia. It is also known that physical exercise, and even daily activity, disrupt glucose dynamics and can generate problems with blood glucose regulation during and after exercise. In order to deal with these challenges, devices for monitoring patients’ physical activity are currently under development. This review focuses on non-invasive sensors using physiological parameters related to physical exercise that were used to improve glucose monitoring in type 1 diabetes (T1DM) patients. These devices are promising for diabetes management. Indeed they permit to estimate glucose concentration either based solely on physical activity parameters or in conjunction with CGM or non-invasive CGM (NI-CGM) systems. In these last cases, the vital signals are used to modulate glucose estimations provided by the CGM and NI-CGM devices. Finally, this review indicates possible limitations of these new biosensors and outlines directions for future technologic developments.
Stefano Bromuri, Serban Puricel, René Schumann, Johannes Krampf, Juan Ruiz, Michael Schumacher
Journal of ambient intelligence and smart environments,
2016, vol. 8, no. 2, pp. 219-237
Purpose: Gestational Diabetes Mellitus (GDM) is a condition affecting 3-4% of pregnant women due to increased resistance to insulin caused by the growth of the fetus. Such a condition disappears just after delivery, but it is an indicator of the insurgence of diabetes type 2 (DT2) later in life: about 40% of the women affected by GDM also develop DT2 . GDM brings several complications during pregnancy to both the mother and the fetus. We aim here at presenting our Personal Health System for monitoring GDM and we also present the results of outpatient monitoring and management by utilizing a personal health system (PHS) for GDM. Methods: The Personal Health System (PHS) was deployed in a feasibility study, modelled as a single-center, parallel group, open randomized controlled trial conducted in Lausanne University Hospital. Patients (n=24) were assigned to 2 different groups: standard protocol group (SP) and telemedicine group (TM). SP patients were managed by regular clinic visits. TM patients were managed with our EPHS system. The targeted feasibility outcome was whole trial feasibility, functioning of the PHS and its appropriateness for patient use. Results: Mean age was 325 years and patients were pregnant for 29.11.9 weeks at study inclusion. Patients came from 16 different countries. The follow-up rate was 100%. Acceptability in the TM-group was high, as 100% were satisfied with the care provided and equally 100% were at ease with the technology. Overall median[IQR] glucose control was 5.4 mmol/l [4.7-6.4] in the TM-group and 5.7mmol/l [4.9-6.7] in the SP-group (p<0.001). Four out of 6 daily plasma glucose values were significantly better controlled with telemedicine compared to standard care. Conclusion: The feasibility study that we conducted shows that PHSs have a great potential to improve the life of the patient by allowing a better communication of their physiological values to the caregivers. With respect to the particular case of GDM, the study suggests that use of PHS technology may improve glycaemic control in GDM, but to confirm this trend, a main trial is needed.
Albert Brugues de la Torre, Stefano Bromuri, Michael Barry, OscarAlfonso Jiménez del Toro, Maciej Mazurkiewicz, Przemyslaw Kardas, Josep Pegueroles-Vallés, Michael Schumacher
Journal of medical systems,
2016, vol. 40, pp. 1-15
Research focus: the focus of this research is in the definition of programmable expert Personal Health Systems (PHS) to monitor patients affected by chronic diseases using agent oriented programming and mobile computing to represent the interactions happening amongst the components of the system. The paper also discusses issues of knowledge representation within the medical domain when dealing with temporal patterns concerning the physiological values of the patient. Research method: in the presented agent based PHS the doctors can personalize for each patient monitoring rules that can be defined in a graphical way. Furthermore, to achieve better scalability, the computations for monitoring the patients are distributed among their devices rather than being performed in a centralized server. The system is evaluated using data of 21 diabetic patients to detect temporal patterns according to a set of monitoring rules defined. The system's scalability is evaluated by comparing it with a centralized approach. Results: The evaluation concerning the detection of temporal patterns highlights the system's ability to monitor chronic patients affected by diabetes. Regarding the scalability, the results show the fact that an approach exploiting the use of mobile computing is more scalable than a centralized approach. Therefore, more likely to satisfy the needs of next generation PHSs. Conclusions: PHSs are becoming an adopted technology to deal with the surge of patients affected by chronic illnesses. This paper discusses architectural choices to make an agent based PHS more scalable by using a distributed mobile computing approach. It also discusses how to model the medical knowledge in the PHS in such a way that it is modifiable at run time. The evaluation highlights the necessity of distributing the reasoning to the mobile part of the system and that modifiable rules are able to deal with the change in lifestyle of the patients affected by chronic illnesses.
Damien Zufferey, Thomas Hofer, Jean Hennebert, Michael Schumacher, Rolf Ingold, Stefano Bromuri
Computers in biology and medicine,
October 2015, vol. 65, pp. 34–43
We are motivated by the issue of classifying diseases of chronically ill patients to assist physicians in their everyday work. Our goal is to provide a performance comparison of state-of-the-art multi-label learning algorithms for the analysis of multivariate sequential clinical data from medical records of patients affected by chronic diseases. As a matter of fact, the multi-label learning approach appears to be a good candidate for modeling overlapped medical conditions, specific to chronically ill patients. With the availability of such comparison study, the evaluation of new algorithms should be enhanced. According to the method, we choose a summary statistics approach for the processing of the sequential clinical data, so that the extracted features maintain an interpretable link to their corresponding medical records. The publicly available MIMIC-II dataset, which contains more than 19,000 patients with chronic diseases, is used in this study. For the comparison we selected the following multi-label algorithms: ML-kNN, AdaBoostMH, binary relevance, classifier chains, HOMER and RAkEL. Regarding the results, binary relevance approaches, despite their elementary design and their independence assumption concerning the chronic illnesses, perform optimally in most scenarios, in particular for the detection of relevant diseases. In addition, binary relevance approaches scale up to large dataset and are easy to learn. However, the RAkEL algorithm, despite its scalability problems when it is confronted to large dataset, performs well in the scenario which consists of the ranking of the labels according to the dominant disease of the patient.
Thomas Hofer, Michael Schumacher, Stefano Bromuri
Swiss Medical Informatics,
September 2015, vol.31, 5 p.
Chronische Krankheiten die in den Formenkreis der Herz-Kreislauf-Erkrankungen gehören oder etwa die chronisch obstruktive Lungenerkrankung (COPD: Chronic Obstructive Pulmonary Disease) zählen zu den häufigsten Todesursachen weltweit. Oft kann der Fortschritt der Krankheit behandelt, aber nicht gestoppt werden, dazu ist eine strikte Einhaltung einer Therapie sowie eine kontinuierliche Überwachung der Vitalparameter notwendig. Dank der Entwicklung Mobiler Endgeräte und deren Evolution in den letzten Jahren kann eine dauerhafte Überwachung des Patienten ohne einen kostspieligen Krankenhausaufenthalt erfolgen. Dies führt im besten Fall zu einer Erhöhung aber zumindest zur Beibehaltung der Lebensqualität des Patienten ohne zu stark in den Alltag eingreifen zu müssen. COMPASS1 (COntinuous Multi-variate Monitoring of Patients Affected by chronic obstructive pulmonary diSeaSe) [1, 2] hat sich zum Ziel gesetzt, ein Personal Health System (PHS) [3, 4] zu entwickeln, dass kontinuierliches Messen von Vitalparametern, Komprimierung der Daten, Interoperabilität und Sicherheit mit einer Prognosekomponente vereint. Diese Publikation präsentiert einen Ansatz, welcher Interoperabilität mit Standards wie zum Beispiel HL7 und gleichermassen Datenkompression mittels Compressive Sensing  ermöglicht.
Albert Brugues de la Torre, Josep Pegueroles-Vallés, Stefano Bromuri, Michael Schumacher
Recent advances in ambient intelligence and context-aware computing
(20 p.). 2015,
Pennsylvania : IGI Global
The development of pervasive healthcare systems consist on applying the ubiquitous computing in the healthcare context. The systems developed in this research field have the goals of offering better healthcare services, promoting well-being of the people and assist healthcare professionals in their tasks. The aim of the present work is to give an overview of the main research efforts in the area of pervasive healthcare systems, and to identify which are the main research challenges in this topic of research. Furthermore, we review the current state of the art for these kind of systems with respect some of the research challenges identified. In particular we focus on contributions done into interoperability, scalability and security of these systems.
Michael Schumacher, Stefano Bromuri, Damien Zufferey, Jean Hennebert
Journal of biomedical informatics,
octobre 2014, vol. 51, pp. 165-175
Objective This research is motivated by the issue of classifying illnesses of chronically ill patients for decision support in clinical settings. Our main objective is to propose multi-label classification of multivariate time series contained in medical records of chronically ill patients, by means of quantization methods, such as bag of words (BoW), and multi-label classification algorithms. Our second objective is to compare supervised dimensionality reduction techniques to state-of-the-art multi-label classification algorithms. The hypothesis is that kernel methods and locality preserving projections make such algorithms good candidates to study multi-label medical time series. Methods We combine BoW and supervised dimensionality reduction algorithms to perform multi-label classification on health records of chronically ill patients. The considered algorithms are compared with state-of-the-art multi-label classifiers in two real world datasets. Portavita dataset contains 525 diabetes type 2 (DT2) patients, with co-morbidities of DT2 such as hypertension, dyslipidemia, and microvascular or macrovascular issues. MIMIC II dataset contains 2635 patients affected by thyroid disease, diabetes mellitus, lipoid metabolism disease, fluid electrolyte disease, hypertensive disease, thrombosis, hypotension, chronic obstructive pulmonary disease (COPD), liver disease and kidney disease. The algorithms are evaluated using multi-label evaluation metrics such as hamming loss, one error, coverage, ranking loss, and average precision. Results Non-linear dimensionality reduction approaches behave well on medical time series quantized using the BoW algorithm, with results comparable to state-of-the-art multi-label classification algorithms. Chaining the projected features has a positive impact on the performance of the algorithm with respect to pure binary relevance approaches. Conclusions The evaluation highlights the feasibility of representing medical health records using the BoW for multi-label classification tasks. The study also highlights that dimensionality reduction algorithms based on kernel methods, locality preserving projections or both are good candidates to deal with multi-label classification tasks in medical time series with many missing values and high label density.
Henning Müller, Sandrine Ding, Bruno Alves, David Godel, Omar Abou Khaled, Francois Mooser, Michael Schumacher
ASTRM actuel (Association Suisse es techniciens en radiologie médicales) - SVMTRA aktuell (Schweizerische Vereinigung der Fachleute für medizinisch technische Radiologie) - ASTRM attualità (Associazione Svizzera dei Tecnici di Radiologia Medica),
Octobre 2014, pp. 18-23
Le projet MediCoordination a permis de contribuer à la stratégie e-Health. L’e-Health est en plein essor en Suisse même si un gros effort devra encore être fourni en termes d’interopérabilité et de standardisation dans la mesure où le dossier patient informatisé est alimenté par des informations provenant d’un nombre considérable de services différents avec des standards tout aussi divers.
Sandrine Ding, Henning Müller, Bruno Alvesa, David Godel, Omar Abou Khaled, François Mooser, Michael Schumacher
2009, vol. 1, p. 1-3
Victor Contreras, Andrea Bagante, Niccolò Marini, Michael Schumacher, Vincent Andrearczyk, Davide Calvaresi
Explainable and transparent AI and multi-agent systems
Lien vers la conférence
Human papillomavirus (HPV) accounts for 60% of head and neck (H&N) cancer cases. Assessing the tumor extension (tumor grading) and determining whether the tumor is caused by HPV infection (HPV status) is essential to select the appropriate treatment. Therefore, developing non-invasive, transparent (trustworthy), and reliable methods is imperative to tailor the treatment to patients based on their status. Some studies have tried to use radiomics features extracted from positron emission tomography (PET) and computed tomography (CT) images to predict HPV status. However, to the best of our knowledge, no research has been conducted to explain (e.g., via rule sets) the internal decision process executed on deep learning (DL) predictors applied to HPV status prediction and tumor grading tasks. This study employs a decompositional rule extractor (namely DEXiRE) to extract explanations in the form of rule sets from DL predictors applied to H&N cancer diagnosis.The extracted rules can facilitate researchers’ and clinicians’ understanding of the model’s decisions (making them more transparent) and can serve as a base to produce semantic and more human-understandable explanations.
Victor Contreras, Michael Schumacher, Davide Calvaresi
Explainable and Transparent AI and Multi-Agent Systems : 4th International Workshop, EXTRAAMAS 2022, Virtual Event, May 9–10, 2022, Revised Selected Papers
Widely used in a growing number of domains, Deep Learning predictors are achieving remarkable results. However, the lack of transparency (i.e., opacity) of their inner mechanisms has raised trust and employability concerns. Nevertheless, several approaches fostering models of interpretability and explainability have been developed in the last decade. This paper combines approaches for local feature explanation (i.e., Contextual Importance and Utility – CIU) and global feature explanation (i.e., Explainable Layers) with a rule extraction system, namely ECLAIRE. The proposed pipeline has been tested in four scenarios employing a breast cancer diagnosis dataset. The results show improvements such as the production of more human-interpretable rules and adherence of the produced rules with the original model.
Davide Calvaresi, Stefan Eggenschwiler, Jean-Paul Calbimonte, Gaetano Manzo, Michael Schumacher
Proceedings of WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
Intelligent systems increasingly support users’ behavior change, including exercise adherence, smoking cessation, and healthy diet adoption. Their effectiveness is affected by the personalization degree of advice/coaching and HMI mechanisms. This paper proposes a personalized agent-based chatbot platform assisting the user in healthy nutrition via pervasive technologies leveraging dynamical, multi-modal, and personalized interactions. The system provides diet recommendations and tracks the user’s food intake and nutritional behaviors to promote a healthy lifestyle. The study concludes with a user study and performance evaluation.
Gaetano Manzo, Davide Calvaresi, Jean-Paul Calbimonte, Okoro Esteem, Michael Schumacher
Proceedings of the 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2021)
Location-based services are essential to delivering information for users in the context of travel, leisure, and sports application. Nevertheless, these services are often implemented as recommendations and suggestions that may overwhelm users, or fail to adapt to their goals, behavior, and context. To address these limitations, this paper presents NearMe, an application that provides tailored recommendations of Points of Interest surrounding the user. Beyond existing approaches, NearMeallows the generation of dynamic recommendations from heterogeneous service providers, and the definition of regions to which notifications are related. Moreover, it allows to fine-tune notifications, thus preventing over-information and noise. A preliminary study has been conducted involving a heterogeneous group of potential users and service providers that elaborates on their vision, expectations, features desiderata, and possible interfaces.
Davide Calvaresi, Giovanni Ciatto, Amro Najjar, Reyhan Aydogan, Leon Van der Torre, Andrea Omicini, Michael Schumacher
Explainable and Transparent AI and Multi-Agent Systems : Third International Workshop, EXTRAAMAS 2021, Virtual Event, May 3–7, 2021, Revised Selected Papers
Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis. However, explanation techniques are still embryotic, and they mainly target ML experts rather than heterogeneous end-users. Furthermore, existing solutions assume data to be centralised, homogeneous, and fully/continuously accessible—circumstances seldom found altogether in practice. Arguably, a system-wide perspective is currently missing. The project named “Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge ” (Expectation) aims at overcoming such limitations. This manuscript presents the overall objectives and approach of the Expectation project, focusing on the theoretical and practical advance of the state of the art of XAI towards the construction of personalised explanations in spite of decentralisation and heterogeneity of knowledge, agents, and explainees (both humans or virtual). To tackle the challenges posed by personalisation, decentralisation, and heterogeneity, the project fruitfully combines abstractions, methods, and approaches from the multi-agent systems, knowledge extraction/injection, negotiation, argumentation, and symbolic reasoning communities.
Randolf Ramseyer, Davide Calvaresi, Benjamin Nanchen, Roland Schegg, Michael Schumacher, Emmanuel Fragnière
Proceedings of the 19th International Conference e-Society 2021
This paper elaborates on a novel concept to orchestrate tourism networks. In particular, each actor optimizes his service (touchpoint n) while seamlessly transferring the customer information from the previous (touchpoint n-1) to the following service (touchpoint n+1). To implement such a theory, we leveraged on chatbot technology, which interfacing directly with the user eased the transition from the point of interaction (touchpoint) n-1 to n+1. Moreover, the chatbot entails the connection between the nodes of the customer's journey, enabling user profiling, personalization, and knowledge transfer. The deployment of a chatbot implementing the "n-1 n+1 touchpoints" model would significantly benefit actors operating in a fragmented touristic economy (i.e., Switzerland). Hence, we tested the first prototype in the heart of the Canton Valais, where, in collaboration with students in tourism, we based the scenario on counterfactual thinking. In particular, we identified all the possible situations a grandmother and her grandson might face arriving at the Sierre train station to spend a day in Crans-Montana. In turn, using reenactment theatre techniques, tourism professionals played the worst-case scenario (without chatbot) and the best case (with the chatbot) to elicit the different clients' perceptions on those diametral situations. Such a feasibility study paved the way to a more holistic view, employing artificial intelligence techniques to enhance the chatbot and smoothen the "n-1 n+1 touchpoints" dynamics.
Davide Calvaresi, Ahmed Ibrahim, Jean-Paul Calbimonte, Roland Schegg, Emmanuel Fragnière, Michael Schumacher
Information and Communication Technologies in Tourism 2021 : Proceedings of the ENTER 2021 eTourism Conference, January 19–22, 2021
In the last decade, Information and Communication Technologies have revolutionized the tourism and hospitality sector. One of the latest innovations shaping new dynamics and fostering a remarkable behavioral change in the interaction between the service provider and the tourist is the employment of increasingly sophisticated chatbots. This work analyzes the most recent systems presented in the literature (since 2016) investigated via 12 research questions. The often appreciated quick evolution of such solutions is the primary outcome. However, such technological and financial fast-pace requires continuous investments, upskilling, and system innovation to tackle the eTourism challenges, which are shifting towards new dimensions.
Jean-Paul Calbimonte, Davide Calvaresi, Michael Schumacher
Proceedings of the 3rd International Workshop on Semantic Web Meets Health Data Management (SWH 2020)
The usage of healthcare data for analytics and patient applications has increased in recent years opening a number of technical, ethical and scientific challenges. Among these, those related to the management of personal and sensitive health data have been addressed through decentralized solutions for patient data, often implemented and modelled using distributed agents and semantic technologies. In this paper, we present a technical summary of our previous works in this area, comprising efforts to: (i) use ontology models to represent patient trajectories,(ii) employ agent-based architectures to model and employ decentralized patient data exchanges, (iii) define agent cooperation and negotiation strategies for healthcare data interactions, (iv) adopt semantic data models for privacy-aware agents, and (v) implement multi-agent systems for real-time healthcare data processing
Davide Calvaresi, Jean-Gabriel Piguet, Jean-Paul Calbimonte, Timotheus Kampik, Amro Najjar, Guillaume Gadek, Michael Schumacher
Proceedings of International Workshops of PAAMS 2020 : Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness.
Intelligent systems are becoming increasingly complex and pervade a broad range of application domains, including safety-critical systems such as e-health, finance, and energy management. Traditional approaches are no longer capable of addressing the demand for trust and transparency in these applications. Hence, the current decade is demanding intelligent systems to be autonomous, and in particular explainable, transparent, and trustworthy. To satisfy such requirements, and therefore to comply with the recent EU regulations in the matter (e.g., GDPR), intelligent systems (e.g., Multi-Agent Systems - MAS) and technologies enabling tamper-proof and distributed consensus (e.g., Blockchain Technology - BCT) are conveying into reconciling solutions. Recently, the empowerment of MAS with BCT (and the use of BCT themselves) has gained considerable momentum, raising challenges, and unveiling opportunities. However, several ethical concerns have yet to be faced. This paper elaborates on the entanglement among ethical and technological challenges while proposing and discussing approaches that address these emerging research opportunities.
Proceedings of the 18th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2020) : Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness
In recent years, we have witnessed the growth of applications relying on the use and processing of personal data, especially in the health and well-being domains. Users themselves produce these data (e.g., through self-reported data acquisition, or personal devices such as smartphones, smartwatches or other wearables). A key challenge in this context is to guarantee the protection of personal data privacy, respecting the rights of users for deciding about data reuse, consent to data processing and storage, anonymity conditions, or the right to withhold or delete personal data. With the enforcement of recent regulations in this domain, such as the GDPR, applications are required to guarantee compliance, challenging current practices for personal data management. In this paper, we address this problem in the context of decentralized personal data applications, which may need to interact and negotiate conditions of data processing and reuse. Following a distributed paradigm without a top-down organization, we propose an agent-based model in which personal data providers and data consumers are embedded into privacy-aware agents capable of negotiating and coordinating data reuse, consent, and policies, using semantic vocabularies for privacy and provenance.
Davide Calvaresi, Giuseppe Albanese, Jean-Paul Calbimonte, Michael Schumacher
The correctness of a system operating in time-constrained scenarios leverages on both precision and delivery time of its outcome. This paper presents SEAMLESS, a system enabling the design, simulation, and in-depth analysis of Multi-Agent Systems (MAS). In particular, SEAMLESS allows defining in detail the agents’ knowledge (set of tasks it might execute), needs (set of tasks to be negotiated), local scheduler (execution of the task-set), negotiation protocols, possible communication delays, and heuristics related to the parameters mentioned above. This tool is pivotal in the strive to study and realize real-time MAS.
Giovanni Ciatto, Michael Schumacher, Andrea Omicini, Davide Calvaresi
Proceedings of the EXTRAAMAS: International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems 2020
Recently, the eXplainable AI (XAI) research community has focused on developing methods making Machine Learning (ML) predictors more interpretable and explainable. Unfortunately, researchers are struggling to converge towards an unambiguous definition of notions such as interpretation, or, explanation—which are often (and mistakenly) used interchangeably. Furthermore, despite the sound metaphors that Multi-Agent System (MAS) could easily provide to address such a challenge, and agent-oriented perspective on the topic is still missing. Thus, this paper proposes an abstract and formal framework for XAI-based MAS, reconciling notions, and results from the literature.
Francesco Alzetta, Paolo Giorgini, Amro Najjar, Michael Schumacher, Davide Calvaresi
In the race for automation, distributed systems are required to perform increasingly complex reasoning to deal with dynamic tasks, often not controlled by humans. On the one hand, systems dealing with strict-timing constraints in safety-critical applications mainly focused on predictability, leaving little room for complex planning and decision-making processes. Indeed, real-time techniques are very efficient in predetermined, constrained, and controlled scenarios. Nevertheless, they lack the necessary flexibility to operate in evolving settings, where the software needs to adapt to the changes of the environment. On the other hand, Intelligent Systems (IS) increasingly adopted Machine Learning (ML) techniques (e.g., subsymbolic predictors such as Neural Networks). The seminal application of those IS started in zero-risk domains producing revolutionary results. However, the ever-increasing exploitation of ML-based approaches generated opaque systems, which are nowadays no longer socially acceptable—calling for eXplainable AI (XAI). Such a problem is exacerbated when IS tend to approach safety-critical scenarios. This paper highlights the need for on-time explainability. In particular, it proposes to embrace the Real-Time Beliefs Desires Intentions (RT-BDI) framework as an enabler of eXplainable Multi-Agent Systems (XMAS) in time-critical XAI.
Giovanni Ciatto, Davide Calvaresi, Michael Schumacher, Andrea Omicini
Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems 2020
We propose an abstract framework for XAI based on MAS encompassing the main definitions and results from the literature, focussing on the key notions of interpretation and explanation.
Davide Calvaresi, Jean-Paul Calbimonte, Fabien Dubosson, Michael Schumacher, Amro Najjar
Proceedings of WI '19 : IEEE/WIC/ACM International Conference on Web Intelligence
Asynchronous messaging is leading human-machine interaction due to the boom of mobile devices and social networks. The recent release of dedicated APIs from messaging platforms boosted the development of computer programs able to conduct conversations, (i.e., chatbots), which have been adopted in several domain-specific contexts. This paper proposes SMAG: a chatbot framework supporting a smoking cessation program (JDF) deployed on a social network. In particular, it details the single-agent implementation, the campaign results, a multi-agent design for SMAG enabling the modelization of personalized behavior and user profiling, and highlighting of coupling chatbot technology with and multi-agent systems.
Jean-Paul Calbimonte, Fabien Dubosson, Michael Schumacher
Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019)
Behavior change is a complex process in which people receive
support in order to improve aspects of their behavior, for instance re-
garding their health or lifestyle. Although there exist several theoretical
approaches to model behavior change, including abstractions that can be
applied to different use-cases, these are not easily translated into reusable
components that can be integrated into implementable systems for per-
suasion. This work discusses the need for an ontology-based approach
to modelling interactions in eHealth systems, with the goal of achieving
behavior change. This contribution includes an analysis of current mod-
elling needs in behavior change, specially regarding: stages of change,
motivation & ability factors, plans & actions, argumentation, and do-
Behavior change is a complex process in which people receive support in order to improve aspects of their behavior, for instance regarding their health or lifestyle. Although there exist several theoretical approaches to model behavior change, including abstractions that can be applied to different use-cases, these are not easily translated into reusable components that can be integrated into implementable systems for persuasion. This work discusses the need for an ontology-based approach to modelling interactions in eHealth systems, with the goal of achieving behavior change. This contribution includes an analysis of current modelling needs in behavior change, specially regarding: stages of change, motivation & ability factors, plans & actions, argumentation, and domain modeling
Jean-Paul Calbimonte, Fabien Dubosson, Ilia Kebets, Pierre-Mikael Legris, Michael Schumacher
Current information technologies allow people to acquire personal data related to their health, lifestyle, behavior, and activities, often using wearable and mobile devices. Personal data management technologies have emerged recently, in order to cope with the requirements of this type of data, ranging from personal clouds to self-storage solutions. Pryv.io is a comprehensive solution for managing this particularly sensible type of data streams, focusing both on data privacy and decentralization. In this paper, we describe SemPryv, a system aiming at providing a semantization mechanism for enriching personal data streams with standardized specialized vocabularies from third-party providers. It relies on third providers of semantic concepts, and includes rule-based mechanisms for facilitating the semantization process. A full implementation of SemPryv has been produced, pluggable to the existing Pryv.io platform, showing the feasibility of the approach.
Alevtina Dubovitskaya, Petr Novotny, Scott Thiebes, Ali Sunyaev, Michael Schumacher, Zhigang Xu, Fusheng Wang
Proceedings of the 45th International Conference on Very Large Data Bases (VLDB) 2019
Abstract. Healthcare is undergoing a big data revolution, with vast amounts of information supplied from numerous sources, leading to ma-jor paradigm shifts including precision medicine and AI driven healthcare among others. Yet, there still exist signiﬁcant barriers before such ap-proaches could be adopted in practice, including data integration and interoperability, data sharing, security and privacy protection, scalabil-ity, policy, and regulations. Blockchain provides a unique opportunity to tackle major challenges in healthcare and biomedical research, such as en-abling data sharing and integration for patient-centered care, data prove-nance allowing veriﬁcation authenticity of the data, and optimization of some of the healthcare processes among others. Nevertheless, technolog-ical constraints of the current blockchain technologies necessitate further research before mass adoption of the blockchain-based healthcare data management is possible. We analyze context-based requirements and ca-pabilities of the available technology and propose a research agenda and new approaches towards achieving intelligent healthcare-data manage-ment using blockchain.
Davide Calvaresi, Yazan Mualla, Amro Najjar, Stéphane Galland, Michael Schumacher
Proceedings of the EXTRAAMAS 2019 International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems
Advances in Artificial Intelligence (AI) are contributing to a broad set of domains. In particular, Multi-Agent Systems (MAS) are increasingly approaching critical areas such as medicine, autonomous vehicles, criminal justice, and financial markets. Such a trend is producing a growing AI-Human society entanglement. Thus, several concerns are raised around user acceptance of AI agents. Trust issues, mainly due to their lack of explainability, are the most relevant. In recent decades, the priority has been pursuing the optimal performance at the expenses of the interpretability. It led to remarkable achievements in fields such as computer vision, natural language processing, and decision-making systems. However, the crucial questions driven by the social reluctance to accept AI-based decisions may lead to entirely new dynamics and technologies fostering explainability, authenticity, and user-centricity. This paper proposes a joint approach employing both blockchain technology (BCT) and explainability in the decision-making process of MAS. By doing so, current opaque decision-making processes can be made more transparent and secure and thereby trustworthy from the human user standpoint. Moreover, several case studies involving Unmanned Aerial Vehicles (UAV) are discussed. Finally, the paper discusses roles, balance, and trade-offs between explainability and BCT in trust-dependent systems.
Michael Barry, Michael Schumacher
Proceedings of the 11th International Conference on Agents and Artificial Intelligence - (Volume 2)
Mathematical solvers have evolved to become complex software and thereby have become a difficult subject for Runtime Prediction and parameter tuning. This paper studies various Machine Learning methods and data generation techniques to compare their effectiveness for both Runtime Prediction and parameter tuning. We show that machine Learning methods and Data Generation strategies that perform well for Runtime Prediction do not necessary result in better results for solver tuning. We show that Data Generation algorithms with an emphasis on exploitation combined with Random Forest is successful and random trees are effective for Runtime Prediction. We apply these methods to a hydro power model and present results from two experiments.
Davide Calvaresi, Alevtina Dubovitskaya, Diego Retaggi, Aldo F. Dragoni, Michael Schumacher
Proceedings of the 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)
Some recent trends in distributed intelligent systems
rely extensively on agent-based approaches. The so-called Multi-
Agent Systems (MAS) are taking over the management of
sensitive data on behalf of their producers and users (e.g., medical
records, financial investment, energy market). Therefore, trusted
interactions are needed more than ever, while accountability and
transparency among the agents seem crucial characteristics to be
achieved. To do so, recent trends advocate the use of blockchain
technologies (BCT) in MAS. The blockchain is a distributed
ledger technology that can execute programmable transaction
logic, and provides a shared, immutable, and transparent appendonly
register of all the actions happening in the network.
Although a few theoretical approaches have already been
proposed, the quest for such a system consolidating BCT and
MAS to guarantee privacy, scalability, transparency, and efficiency
continues. This paper presents a reconciling system
including BCT within the dynamics of a MAS. Such a system
aims at (i) building a solid ground for trusted interactions and
(ii) enabling more characterizing feature-based and trustworthy
ways of computing agent reputation. The system has been tested
in four scenarios with different configurations (regular executions
and involving down-agents or malicious behaviors).
Finally, the paper summarizes and discusses the experience
gained, argues about the strategic choice of binding MAS and
BCT, and presents some future challenges.
Davide Calvaresi, Giuseppe Albanese, Mauro Marinoni, Fabien Dubosson, Paolo Sernani, Aldo Franco Dragoni, Michael Schumacher
Proceedings of the 1st International Workshop on Real-Time compliant Multi-Agent Systems co-located with the Federated Artificial Intelligence Meeting
In the last decades, the use of Multi-Agent Systems (MAS) resulted in being the most relevant approach to foster the development of systems performing distributed reasoning, automated/autonomous actions, and regulating component interactions in unpredictable and uncertain scenarios. The scientific community provided numerous innovative contributions about resource and task allocation seeking for optimal/sub-optimal solutions. The adoption of MAS in Cyber- Physical Systems (CPS) is producing outstanding results. However, in current MAS, the actual task execution is still delegated to traditional general-purpose scheduling algorithms running within the agent (local scheduler of behaviors). The main consequence is the incapability to enforce compliance with strict timing constraints (i.e., the impossibility of providing any guarantee about the system’s behavior in the worst-case scenario). Therefore, the adoption of MAS is hampered, excluding significant application scenarios such as safety-critical environments. This paper proposes the schedulability analysis of various task-sets, that are feasible using real-time schedulers, on top of traditional general-purpose solutions. In particular, the study of deadline-missing rate occurring in general-purpose setups, evaluated on an agent-based simulator developed on OMNET++, named MAXIM-GPRT, is presented. The obtained results strengthen the motivations for adopting and adapting real-time scheduling mechanisms as the local scheduler within agents.
Davide Calvaresi, Giuseppe Albanese, Fabien Dubosson, Mauro Marinoni, Michael Schumacher
The adoption of Multi-Agent Systems (MAS) is permeating Internet of Things (IoT) and Cyber-Physical Systems (CPS). Timing reliability of MAS is a daring challenge. The study of local task execution and negotiation of workloads are catalyzing considerable interest. By adopting techniques typical of Real-Times Systems (RTS), MAS’s ability to comply with strict timing constraints has been proven. However, a complete formalization is still missing, and some of the existing mathematical models introduce considerable pessimism in the performance analysis. Therefore, the need for tools supporting the study of the behavior of agent-based systems is rising. Particularly, the capability of systematic assessment and comparison of their performance. This paper presents a system to generate task-sets and operating scenarios, to support the study of timing reliability, behavior, and performance of MAS. The parameters required for such a generation are characterized by randomly extracted values (e.g., the number of agents, single agent utilization factors, and single task utilization factor). For each parameter, it is possible to select a given statistical distribution to be applied to user-defined ranges. In particular, logic, constraints, and dependencies characterizing the generation algorithm are detailed and framed in a functional work-flow. Moreover, such a system integrates a MAS simulator powered by both general-purpose and real-time algorithms, named MAXIMGPRT. Hence, the presented tool is also able to show the logs of the tested scenarios equipped with graphs to enable the performance analysis.
Davide Calvaresi, Mauro Marinoni, Luca Lustrissimini, Kevin Appoggetti, Paolo Sernani, Michael Schumacher, Giorgio Buttazzo
Proceedings of 15th European Conference on Multi-Agent Systems (EUMAS 2017)
Multi-Agent Systems (MAS) have been supporting the development of distributed systems performing decentralized thinking and reasoning, automated actions, and regulating component interactions in unpredictable and uncertain scenarios. Despite the scientific literature is plenty of innovative contributions about resource and tasks allocation, the agents still schedule their behaviors and tasks by employing traditional general-purpose scheduling algorithms. By doing so, MAS are unable to enforce the compliance with strict timing constraints. Thus, it is not possible to provide any guarantee about the system behavior in the worst-case scenario. Thereby, as they are, they cannot operate in safety-critical environments. This paper analyzes the agents' local schedulers provided by the most relevant agent-based frameworks from a cyber-physical systems point of view. Moreover, it maps a set of agents' behaviors on task models from the real-time literature. Finally, a practical case-study is provided to highlight how such "MAS reliability" can be achieved.
Alevtina Dubovitskaya, Xu Zhigang, Samuel Ryu, Michael Schumacher, Fusheng Wang
Proceedings of the AMIA annual symposium 2017
Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research studies. We propose a framework on managing and sharing EMR data for cancer patient care. In collaboration with Stony Brook University Hospital, we implemented our framework in a prototype that ensures privacy, security, availability, and fine-grained access control over EMR data. The proposed work can significantly reduce the turnaround time for EMR sharing, improve decision making for medical care, and reduce the overall cost.
Alevtina Dubovistkaya, Thierry Buclin, Michael Schumacher, Karl Aberer, Switzerland Lausanne, Yann Thoma, Switzerland Yverdon-les-Bains
Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
Therapeutic Drug Monitoring (TDM) is a key concept in precision medicine. The goal of TDM is to avoid therapeutic failure or toxic effects of a drug due to insufficient or excessive circulating concen-tration exposure related to between-patient variability in the drug’s disposition. We present TUCUXI – an intelligent system for TDM. By making use of embedded mathematical models, the software allows to compute maximum likelihood individual predictions of drug concentrations from population pharmacokinetic data, based on patient’s parameters and previously observed concentrations. TUCUXI was developed to be used in medical practice, to assist clinicians in taking dosage adjustment decisions for optimizing drug concentration levels. This software is currently being tested in a University Hospital. In this paper we focus on the process of software integration in clinical workflow. The modular architec-ture of the software allows us to plug in a module enabling data aggregation for research purposes. This is an important feature in order to develop new mathematical models for drugs, and thus to improve TDM. Finally we discuss ethical issues related to the use of an automated decision support system in clinical practice, in particular if it allows data aggregation for research purposes.
Davide Calvaresi, Mauro Marinoni, Arnon Sturm, Michael Schumacher, Giorgio Buttazzo
Proceedings of the International Conference on Web Intelligence (WI '17)
Techniques originating from the Internet of Things (IoT) and Cyber-Physical Systems (CPS) areas have extensively been applied to develop intelligent and pervasive systems such as assistive monitoring, feedback in telerehabilitation, energy management, and negotiation. Those application do-mains particularly include three major characteristics: intel-ligence, autonomy and real-time behavior. Multi-Agent Sys-tems (MAS) are one of the major technological paradigms that are used to implement such systems. However, they mainly address the first two characteristics, but miss to com-ply with strict timing constraints. The timing compliance is crucial for safety-critical applications operating in domains such as healthcare and automotive. The main reasons for this lack of real-time satisfiability in MAS originate from cur-rent theories, standards, and technological implementations. In particular, internal agent schedulers, communication mid-dlewares, and negotiation protocols have been identified as co-factors inhibiting the real-time compliance. This paper provides an analysis of such MAS components and pave the road for achieving the MAS compliance with strict timing constraints, thus fostering reliability and predictability.
Alevtina Dubovitskaya, Thierry Buclin, Michael Schumacher, Karl Aberer, Yann Thoma
Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 20-23 August 2017, Boston, Massachusetts, USA
Therapeutic Drug Monitoring (TDM) is a key concept in precision medicine. The goal of TDM is to avoid therapeutic failure or toxic effects of a drug due to insufficient or excessive circulating concen-tration exposure related to between-patient variability in the drug’s disposition. We present TUCUXI – an intelligent system for TDM. By making use of embedded mathematical models, the software allows to compute maximum likelihood individual predictions of drug concentrations from population pharmacokinetic data, based on patient’s parameters and previously observed concentrations. TUCUXI was developed to be used in medical practice, to assist clinicians in taking dosage adjustment decisions for optimizing drug concentration levels. This software is currently being tested in a University Hospital. In this paper we focus on the process of software integration in clinical work ow. The modular architecture of the software allows us to plug in a module enabling data aggregation for research purposes. This is an important feature in order to develop new mathematical models for drugs, and thus to improve TDM. Finally we discuss ethical issues related to the use of an automated decision support system in clinical practice, in particular if it allows data aggregation for research purposes.
Fabien Dubosson, Natalia Mordanyuk, Beatriz López, Michael Schumacher
Proceedings of the Second Workshop on Artificial Intelligence for Diabetes
Diabetic patients usually take insulin bolus right before eat-ing a meal. A wrong dosage of insulin may lead to a hypoglycemia. Be-ing able to anticipate such insulin-induced, postprandial hypoglycemias would enable warning of the patients about the risk associated with the quantity of insulin they are planning to take. In this work, we explore the feasibility of predicting these postprandial hypoglycemias by using information available at pre-meal time, such as glucose levels, planned insulin intakes and carbohydrates estimations. First, an experiment has been done on a dataset acquired on real patients, for which several classes of machine learning algorithms have been tried. The obtained results do not offer predictions that are useful enough to consider any usage in real-life applications. These kinds of datasets — acquired on real patients — suffer heavily from missing data and incorrect carbohydrates estimations though. In order to analyse the impact of these flaws on the obtained results, the same experiment has been run on a simulated dataset. Re-sults support that even with the simulated dataset, which does not have missing data and which has precise carbohydrates intake, these features alone are not able to predict postprandial hypoglycemia. Therefore, im-proving the quality of patients annotations is not enough to solve the problem, and using these features without further features engineering does not offer good results.
Davide Calvaresi, Michael Schumacher, Mauro Marinoni, Roger Hil?ker, Aldo F. Dragoni, Giorgio Buttazzo
Proceedings of X Workshop on Agents Applied in Health Care, A2HC 2017
Telerehabilitation in older adults is most needed in the patient environments, rather than in formal ambulatories or hospitals. Sup-porting such practices brings significant advantages to patients, their family, formal and informal caregivers, clinicians, and researchers. Sev-eral techniques and technologies have been developed aiming at facilitat-ing and enhancing the eﬀectiveness of telerehabilitation. This paper gives a quick overview of the state of the art, investigating video-based, wear-able, robotic, distributed, and gamified telerehabilitation solutions. In particular, agent-based solutions are analyzed and discussed addressing strength, limitations, and future challenges. Elaborating on functional requirements expressed by professional physiotherapists and researchers, the need for extending multi-agent systems (MAS) peculiarities at the sensing level in wearable solutions establishes new research challenges. Employed in cyber-physical scenarios with users-sensors and sensors-sensors interactions, MAS are requested to handle timing constraints, scarcity of resources and new communication means, which are crucial for providing real-time feedback and coaching.
Nicola Falcionelli, Albert Brugués, Paolo Sernani, Michael Schumacher, Aldo Franco Dragoni
Proceedings of International Workshop Analysis of Biometric Parameters to detect relationship between stress and sleep quality (AnBiPa 2016)
Fabien Dubosson, Stefano Bromuri, Michael Schumacher
Proceedings of the 30th IEEE International Conference on Advanced Information Networking and Applications (AINA) 2016
Machine learning domain has grown quickly the last few years, in particular in the mobile eHealth domain. In the context of the DINAMO project, we aimed to detect hypoglycemia on Type 1 diabetes patients by using their ECG, recorded with a sport-like chest belt. In order to know if the data contain enough information for this classification task, we needed to apply and evaluate machine learning algorithms on several kinds of features. We have built a Python toolbox for this reason. It is built on top of the scikit-learn toolbox and it allows evaluating a defined set of machine learning algorithms on a defined set of features extractors, taking care of applying good machine learning techniques such as cross-validation or parameters grid-search. The resulting framework can be used as a first analysis toolbox to investigate the potential of the data. It can also be used to fine-tune parameters of machine learning algorithms or parameters of features extractors. In this paper we explain the motivation of such a framework, we present its structure and we show a case study presenting negative results that we could quickly spot using our toolbox.
Michael Schumacher, Albert Brugués, Stefano Bromuri, Josep Pegueroles
Swiss Medical Informatics (SMI) Conference Swiss eHealth Summit 2015
Nowadays, the population is ageing, which together with current lifestyles is contributing to a major prevalence of chronic diseases. In this scenario it is important to provide good healthcare services without increasing associated costs. A prominent way to tackle this challenge is through the application of personal health systems (PHSs), which provide monitoring technologies to patients in order to help them with the self-management of chronic diseases. In this context, agents can simplify the modelling of PHSs as they are autonomous software entities that pursue a set of goals in an intelligent way, by applying artificial intelligence reasoning techniques. In the MAGPIE project we propose the use of multiagent systems (MAS) in PHSs as a solution for monitoring patients affected by chronic diseases.
Michael Schumacher, Visara Urovi
Motivating Scenario: The metabolic syndrome (MS) is a cluster of health conditions that occur together and increase the risk of heart disease, stroke and diabetes. As the availability of wearable sensors is becoming more popular, the collection of frequent physiological data from individuals has become easier than ever. This raises a need for new models that interpret continuous physiological values and provide meaningful interpretation for patients and caregivers. One way of interpreting these data is by automating existing evidence based guidelines. The assumption is that, by combining different clinical guidelines relating to the metabolic syndrome with the physiological data of the patient, we can predict deterioration states that may require medical attention. Such solution can assist caregivers in identifying high-risk patients and provide patient tailored interventions.
Bruno Alves, Samuel Gaillard, Johannes Krampf, Fabian Imsand, Michael Schumacher
Le travail du médecin généraliste consiste souvent à faire face à l’incertitude. Il est régulièrement confronté à de multiples hypothèses, qu’il cherche à étayer par des observations, ses connaissances ou son expérience. Parfois, cette confrontation peut générer un stress, rendre la consultation inefficace ou une trop grande utilisation de ressources. Dans ces cas limite, sa connaissance ne suffit plus et il doit alors s’en remettre à des procédures écrites basées sur la pratique ou sur l’évidence médicale: les guides de pratique. Cet article est structuré en 4 parties. La section suivante introduit la motivation d’un tel projet, ainsi que la méthodologie utilisée. La partie suivante détaille les composants essentiels de la technologie. Une courte discussion en fin de papier renseigne sur les points manquants pour finir sur la conclusion du travail.
Damien Zufferey, Stefano Bromuri, Michael Schumacher
Proceedings of Workshop on Personal Health Systems for Chronic Diseases (PHSCD) at Pervasive Health 2015
Patients suffering from diabetes often develop several comorbidities such as hypertension and dyslipidemia. The presence of the comorbidities leads to more complex patient profiles associated with specific patient treatments. In this paper we present a novel algorithm to help physicians, given a new case, in retrieving similar past patient cases. This novel algorithm is based on the bag-of-words (BoW) model to encode as features, the occurrence of each pre-computed cluster, for each patient, according to the approach of document classification. We then evaluate the algorithm on a real de-identified dataset of 3201 diabetic patients, demonstrating the advantage of our approach.
Alevtina Dubovitskaya, Visara Urovi, Matteo Vasirani, Karl Aberer, Michael Schumacher
Proceedings of the 30th Information Security and Privacy (IFIP) International Securities and Exchange Commission (SEC) Conference, Technical Committee 11 (SEC TC-11) on ICT Systems Security and Privacy Protection (IFIP TC-11 SEC) 2015
In this paper, we address the problem of building an anonymized medical database from multiple sources. Our proposed solution defines how to achieve data integration in a heterogeneous network of many clinical institutions, while preserving data utility and patients’ privacy. The contribution of the paper is twofold: Firstly, we propose a secure and scalable cloud eHealth architecture to store and exchange patients’ data for the treatment. Secondly, we present an algorithm for efficient aggregation of the health data for the research purposes from multiple sources independently.
Alevtina Dubovitskaya, Visara Urovi, Karl Aberer, Michael Schumacher
Proceedings of the 9th Workshop on Agents Applied in Health Care (A2HC), held in conjuntion with the International Conference on Autonomous Agents & Multiagent Systems (AAMAS) 2015
This paper presents a model of a MAS framework for dynamic aggregation of population health data for research purposes. The contribution of the paper is twofold: First, it describes a MAS architecture that allows one to built on the fly anonymized databases from the distributed sources of data. Second, it shows how to improve the utility of the data with the growth of the database.
Proceedings of Workshop on Personal Health Systems for Chronic Diseases 2015
In the past years the progress on the mobile market has made possible an advancement in terms of telemedicine systems and definition of systems for monitoring chronic illnesses. The distribution of mobile devices in developed countries is increasing. Many of these devices are equipped with wireless standards including Bluetooth and the amount of sold Smartphones is constantly increasing. Our approach is oriented towards this market, using existing devices to enable in-home patient monitoring and even further to ubiquitious monitoring. The idea is to increase the quality of care, reduce costs and gather medical grade data, especially vital signs, with a resolution of minutes or even less, which is nowadays only possible in an ICU (Intensive Care Units). In this paper we will present the COMPASS personal health system (PHS) platform, and how this platform enables Android devices to collect, analyze and send sensor data to an observation storage by means of interoperability standards. Furthermore, we will also present how this data can be compressed using advanced compressed sensing techniques and how to optimize these techniques with genetic algorithms to improve the RMSE of the reconstructed signal after compression. We also produce a preliminary evaluation of the algorithm against the state of the art algorithms for compressed sensing.
Albert Brugues de la Torre, Stefano Bromuri, Josep Pegueroles-Vallés, Michael Schumacher
Proceedings of the 15th International HL7 Interoperability Conference
Background: Pervasive healthcare is a new paradigm of healthcare services where the patients are active participants on their own well being. The development of Pervasive Healthcare Systems (PHSs) consists on approaching monitoring solutions into the hands of the patients, and has been reported as a key to minimize the healthcare costs due to the aging of population. However, interoperability is a technological challenge not taken into account in most of the existing implementations of PHSs. Objectives: This paper focuses on how we provide interoperability to a PHS for the management of the gestational diabetes mellitus (GDM) by using the CDA standard. In this monitoring system an Android application sends CDA documents to the server side of the system, so that the health information reported by the patient is transmitted over the Internet in an interoperable way.
Danny Weyns, Van Dick Parunak, Olivier Boissier, Fabien Michel, Michael Schumacher, Alessandro Ricci, et al.
Agent Environments for Multi-Agent Systems IV : proceedings of the 4th International Workshop Environments for Multiagent Systems (E4MAS) 2014
Ten years ago, researchers in multi-agent systems became more and more aware that agent systems consist of more than only agents. The series of workshops on Environments for Multi-Agent Systems (E4MAS 2004-2006) emerged from this awareness. One of the primary outcomes of this endeavor was a principled understanding that the agent environment should be considered as a primary design abstraction, equally important as the agents. A special issue in JAAMAS 2007 contributed a set of influential papers that define the role of agent environments, describe their engineering, and outline challenges in the field that have been the drivers for numerous follow up research efforts. The goal of this paper is to wrap up what has been achieved in the past 10 years and identify challenges for future research on agent environments. Instead of taking a broad perspective, we focus on three particularly relevant topics of modern software intensive systems: large scale, openness, and humans in the loop. For each topic, we reflect on the challenges outlined 10 years ago, present an example application that highlights the current trends, and from that outline challenges for the future. We conclude with a roadmap on how the different challenges could be tackled.
Alevtina Dubovitskaya, Visara Urovi, Michael Schumacher, Matteo Vasirani, Karl Aberer, Aline Fuchsc, Thierry Buclin, Yann Thoma
Swiss Medical Informatics (SMI) Conference Swiss eHealth Summit 2014
The treatment of certain diseases such as cancer, HIV, or other serious medical conditions relies on a regular administration of critical drugs that are necessary to keep those life-threatening diseases under control. Those drugs (e.g. Efavirenz, Imatinib, Tacrolimus, Tobramycin) have a narrow therapeutic range and a poorly predictable relationship between the dose and the blood drug concentration, which may vary greatly among individuals. Therapeutic Drug Monitoring (TDM) aims at improving patient care by monitoring drug levels in the blood to individually adjust the dosage for targeting drug concentration in the therapeutic interval. In order to ensure a better prediction of the relationship between dose and drug concentration, the ISyPeM2 project (a continuation of the Nano-Tera project: Intelligent Integrated Systems for Personalized Medicine, ISyPeM, http://www.nano-era.ch/projects/368.php) has developed a Bayesian TDM approach [GWM+12] based on studies in general or special populations. This approach requires population health data (covariates, dosages, drug concentrations) to be collected and analysed by researchers, in order to enhance the prediction models. Therefore the following question arises: how is it possible to share and aggregate medical data for research purposes while preserving the patients’ privacy?