A Machine Learning Approach to Management of Heart Failure Populations

• Published in: JACC. Heart failure Vol. 8; no. 7; pp. 578 - 587
• Main Authors: Jing, Linyuan, Ulloa Cerna, Alvaro E., Good, Christopher W., Sauers, Nathan M., Schneider, Gargi, Hartzel, Dustin N., Leader, Joseph B., Kirchner, H. Lester, Hu, Yirui, Riviello, David M., Stough, Joshua V., Gazes, Seth, Haggerty, Allyson, Raghunath, Sushravya, Carry, Brendan J., Haggerty, Christopher M., Fornwalt, Brandon K.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2020
• Subjects: Aged; Aged, 80 and over; data science; Disease Management; electronic health records; Female; Heart Failure - epidemiology; Heart Failure - therapy; Humans; Machine Learning; Male; Middle Aged; Morbidity - trends; population health; Population Surveillance - methods; Retrospective Studies; Risk Assessment - methods; ROC Curve; United States - epidemiology; United States; population health; ACE; CRT; ECG; electronic health records; HFpEF; HFrEF; BP; EHR; AUC; ARB; data science; EBBB; HF

Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies. This study sought to generate a strategy for managing populations of patients with heart failure by leveraging large clinical datasets and machine learning. Geisin...