Early prediction of patient outcomes is important for targeting preventive care. This protocol
describes a practical workflow for develo** deep-learning risk models that can predict …

Objective To describe and appraise the use of artificial intelligence (AI) techniques that can
cope with longitudinal data from electronic health records (EHRs) to predict health-related …

Importance Because effective interventions to reduce hospital readmissions are often
expensive to implement, a score to predict potentially avoidable readmissions may help …

Background Unlike linear models which are traditionally used to study all-cause mortality,
complex machine learning models can capture non-linear interrelations and provide …

Abstract Machine learning have been widely adopted in a variety of fields including
engineering, science, and medicine revolutionizing how data is collected, used, and stored …

Understanding the correlation between the fundamental descriptors and catalytic
performance is meaningful to guide the design of high‐performance electrochemical …

The COVID-19 pandemic has placed unprecedented strain on the healthcare system,
particularly hospital bed capacity in the setting of large variations in patient length of stay …

Background Unwarranted extended length of stay (LOS) increases the risk of hospital-
acquired complications, morbidity, and all-cause mortality and needs to be recognized and …

Hospital readmission prediction is a study to learn models from historical medical data to
predict probability of a patient returning to hospital in a certain period, eg 30 or 90 days, after …

Objectives The authors implemented an explainable machine learning (ML) model to gain
insight into the association between cardiac magnetic resonance markers and adverse …