Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes

Abstract

Key Points Question Can prediction of patient outcomes in heart failure based on routinely collected claims data be improved with machine learning methods and incorporating linked electronic medical records? Findings In this prognostic study including records on 9502 patients, machine learning methods offered only limited improvement over logistic regression in predicting key outcomes in heart failure based on administrative claims. Inclusion of additional predictors from electronic medical records improved prediction for mortality, heart failure hospitalization, and loss in home days but not for high cost. Meaning Models based on claims-only predictors may achieve modest discrimination and accuracy in prediction of key patient outcomes in heart failure, and machine learning approaches and incorporation of additional predictors from electronic medical records may offer some improvement in risk prediction of select outcomes.