• Multidisciplinary partnership between technical experts and end-users, including clinicians,
administrators, and patients and their families, is essential to developing and implementing …

Objective To identify factors influencing implementation of machine learning algorithms
(MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a …

As machine learning (ML) shows vast potential in real world applications, the number of
deployed models has been increasing substantially, but little attention has been devoted to …

OBJECTIVES: Hospital early warning systems that use machine learning (ML) to predict
clinical deterioration are increasingly being used to aid clinical decision-making. However, it …

Machine learning (ML) solutions are increasingly entering healthcare. They are complex,
sociotechnical systems that include data inputs, ML models, technical infrastructure and …

Background: The implementation and clinical impact of machine learning–based early
warning systems for patient deterioration in hospitals have not been well described. We …

Healthcare AI faces an ethical dilemma between selective and equitable deployment,
exacerbated by flawed performance metrics. These metrics inadequately capture real-world …

Background Deploying safe and effective machine learning models is essential to realize
the promise of artificial intelligence for improved healthcare. Yet, there remains a large gap …

• L'apprentissage machine a le potentiel de transformer le domaine de la santé, mais ses
applications en médecine clinique sont pour le moment limitées.• Les partenariats …

Time series data captures temporal patterns and provides invaluable insights into various
dynamic phenomena. In healthcare, time series plays a crucial role in understanding the …