Chun Pan Hon
ABSTRACT
Being able to stratify patients according to 30-day hospital readmission risk, anticipated length and cost of stay can guide clinicians in discharge planning and intervention recommendation, leading to an increase of quality of care, and a decrease of healthcare cost. We present a comparative performance analysis of decision trees, boosted decision trees and logistic regression models that can flag, at the time of discharge, patients with an anticipated early, lengthy and expensive readmission. We validate our models using discharge records of 500K congestive heart failure patients from California-licensed hospitals.
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Index Terms
- Risk Stratification for Hospital Readmission of Heart Failure Patients: A Machine Learning Approach
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