Machine Learning Methods Improve Prognostication Identify Clinically Distinct Phenotypes and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients

摘要

BackgroundWhereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patients would improve prognostication of outcomes, identify distinct patient phenotypes, and detect heterogeneity in treatment response.