Machine Learning Prediction of 90-Day Readmission and Mortality in General Internal Medicine: A Retrospective Cohort Study

Background/Objectives: Predicting post-discharge outcomes in general internal medicine (GIM) is essential for targeted intervention. This study developed machine learning (ML) models to predict 90-day hospital readmission and 90-day all-cause post-discharge mortality using routine clinical data.

Methods

This retrospective cohort study included 935 adult admissions to a tertiary GIM department (January 2022-December 2023), of which 881 were retained after data quality verification. Six ML algorithms-Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), Neural Network, and Voting Ensemble-were evaluated across admission-only (Tier 1) and discharge-augmented (Tier 2) feature sets using Synthetic Minority Over-sampling Technique (SMOTE), SelectKBest feature selection, and five-fold cross-validated hyperparameter tuning.