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PEOPLE@HES-SO - Verzeichnis der Mitarbeitenden und Kompetenzen
PEOPLE@HES-SO - Verzeichnis der Mitarbeitenden und Kompetenzen

PEOPLE@HES-SO
Verzeichnis der Mitarbeitenden und Kompetenzen

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Monti Matteo

Monti Matteo

Professeur HES associé

Hauptkompetenzen

Solaire thermique

Science des matériaux

Energy storage

Montage et gestion de projet

Projets de recher

Thin Films and Surface Treatment

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  • Lehre

  • Publikationen

  • Konferenzen

Hauptvertrag

Professeur HES associé

Telefon-Nummer: +41 24 557 75 77

Büro: S06a

Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud
Route de Cheseaux 1, 1400 Yverdon-les-Bains, CH
HEIG-VD
Institut
IE - Institut des énergies
MSc HES-SO en Engineering - HES-SO Master
  • Solaire Thermique
BSc HES-SO en Energie et techniques environnementales - Haute école d'Ingénierie et de Gestion du Canton de Vaud
  • Solaire Thermique

2025

Does the use of structured interventions to guide ward rounds affect patient outcomes? :
Wissenschaftlicher Artikel ArODES
a systematic review

Victoria Ando, Alexia Cavin-Trombert, David Gachoud, Matteo Monti

BMJ quality & safety,  2025, 35, 1, 50-62

Link zur Publikation

Zusammenfassung:

ackground Ward rounds are an essential activity occurring in hospital settings. Despite their fundamental role in guiding patient care, they have no standardised approach. Implementation of structured interventions during ward rounds was shown to improve outcomes such as efficiency, documentation and communication. Whether these improvements have an impact on clinical outcomes is unclear. Our systematic review assessed whether structured interventions to guide ward rounds affect patient outcomes. Methods A systematic search was carried out in May 2023 on Embase, Medline, CINAHL, ERIC, Web of Science Core Collection, the Cochrane Library (Wiley) and Google Scholar, and a backward and forward citation search in January 2024. We included peer-reviewed, original studies assessing the use of structured interventions during bedside ward rounds (BWRs) on clinical outcomes. All inpatient hospital settings where BWRs are performed were included. We excluded papers looking at board, teaching or medication rounds. Results Our search strategy yielded 29 studies. Two were randomised controlled trials (RCTs) and 27 were quasi-experimental interventional studies. The majority (79%) were conducted in intensive care units. The main clinical outcomes reported were mortality, infectious complications, length of stay (LOS) and duration of mechanical ventilation (DoMV). Mortality, LOS and rates of urinary tract and central-line associated bloodstream infections did not seem to be affected, positively or negatively, by interventions structuring BWRs, while evidence was conflicting regarding their effects on rates of ventilator-associated pneumonia and DoMV, with a signal towards improved outcomes. Studies were generally of low-to-moderate quality. Conclusion The impact of structured interventions during BWRs on clinical outcomes remains inconclusive. Higher quality research focusing on multicentric RCTs or on prospective pre–post trials with concurrent cohorts, matched for key characteristics, is needed.

2023

Byzantine-Resilient learning beyond gradients :
Konferenz ArODES
distributing evolutionary search

Andrei Kucharavy, Matteo Monti

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Link zur Konferenz

Zusammenfassung:

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them. Such a drastic increase led to a growing interest in distributed ML, which in turn made worker failures and adversarial attacks an increasingly pressing concern. While distributed byzantine resilient algorithms have been proposed in a differentiable setting, none exist in a gradient-free setting. The goal of this work is to address this shortcoming. For that, we introduce a more general definition of byzantine-resilience in ML- the model-consensus, that extends the definition of the classical distributed consensus. We then leverage this definition to show that a general class of gradient-free ML algorithms - (1, 𝜆)-Evolutionary Search - can be combined with classical distributed consensus algorithms to generate gradient-free byzantine-resilient distributed learning algorithms. We provide proofs and pseudo-code for two specific cases - the Total Order Broadcast and proof-of-work leader election. To our knowledge, this is the first time a byzantine resilience in gradient-free ML was defined, and algorithms to achieve it – were proposed

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