Context Predicting hospital readmission risk is of great interest to identify which patients
would benefit most from care transition interventions, as well as to risk-adjust readmission …

New financial penalties for institutions with high readmission rates have intensified efforts to
reduce rehospitalization. Several interventions that involve multiple components (eg, patient …

Physicians make critical time-constrained decisions every day. Clinical predictive models
can help physicians and administrators make decisions by forecasting clinical and …

Predictive modeling with electronic health record (EHR) data is anticipated to drive
personalized medicine and improve healthcare quality. Constructing predictive statistical …

Background The broad adoption of electronic health records (EHRs) provides great
opportunities to conduct health care research and solve various clinical problems in …

Since the inception of the Canadian Cardiovascular Society heart failure (HF) guidelines in
2006, much has changed in the care for patients with HF. Over the past decade, the HF …

Health care applications of artificial intelligence (AI) have recently emerged. Artificial
intelligence approaches, such as deep learning, rely on vast amounts of data and complex …

Aims This study aimed to review the performance of machine learning (ML) methods
compared with conventional statistical models (CSMs) for predicting readmission and …

BACKGROUND AND OBJECTIVE: Health care use of children with medical complexity
(CMC), such as those with neurologic impairment or other complex chronic conditions …

Importance Health care services that support the hospital-to-home transition can improve
outcomes in patients with heart failure (HF). Objective To test the effectiveness of the Patient …