Early prediction of patient discharge disposition in acute neurological care using machine learning

Abstract

Highlights The study focuses on providing early prediction of discharge locations for patients in acute care settings with severe neurological conditions based on data that are available within the 24 h of patient’s hospital admission. Two separate experiments were considered with separate discharge outcome classes: a binary outcome (home vs. non-home) and a multi-class outcome (home, nursing facility, rehab, death). Five machine learning models were developed to utilize with each experiment and provide detailed accuracy reports (confusion matrices, ROC curves, etc.). The study also investigates the accuracy, reliability, and interpretability of the best-performing models by identifying and analyzing the features that are most impactful to the predictions.

Keywords

• Machine learning
• Discharge planning
• Interpretability
• Neurological