Background Heart failure is a prevalent, costly disease for which new value-based payment
models demand optimized population management strategies. Objectives This study sought …

Objective The partnership between humans and machines can enhance clinical decisions
accuracy, leading to improved patient outcomes. Despite this, the application of machine …

Importance Accurate risk stratification of patients with heart failure (HF) is critical to deploy
targeted interventions aimed at improving patients' quality of life and outcomes. Objectives …

Importance Traditional models for predicting in-hospital mortality for patients with heart
failure (HF) have used logistic regression and do not account for social determinants of …

Background Identifying heart failure patients most likely to suffer poor outcomes is an
essential part of delivering interventions to those most likely to benefit. We sought a …

Background There is a need to develop electronic health record–based predictive models
for worsening heart failure (WHF) events across clinical settings and across the spectrum of …

Background Whereas heart failure (HF) is a complex clinical syndrome, conventional
approaches to its management have treated it as a singular disease, leading to inadequate …

Heart failure (HF) is a leading cause of hospitalizations and readmissions in the United
States. Particularly among the elderly, its prevalence and costs continue to rise, making it a …

Background The ability to predict risk allows healthcare providers to propose which patients
might benefit most from certain therapies, and is relevant to payers' demands to justify …

Background Predicting mortality is important in patients with heart failure (HF). However,
current strategies for predicting risk are only modestly successful, likely because they are …