OBJECTIVES: This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI).
Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the health...
BACKGROUND: Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been develo...
BACKGROUND: Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive ...
Chronic obstructive pulmonary disease (COPD) yields a high rate of failures such as hospital readmission and death in the United States, Canada and worldwide. COPD failure imposes a significant social and economic burden on society, and predicting su...
IMPORTANCE: Hospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores.
AIMS: Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results...
BACKGROUND: As machine learning becomes increasingly common in health care applications, concerns have been raised about bias in these systems' data, algorithms, and recommendations. Simply put, as health care improves for some, it might not improve ...
In BriefAuthors of this study analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients with an accuracy >95% when using all variables and >79% when using only predischarge variables. A model...