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

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. Results: The 90-day readmission and mortality rates were 33.4% and 4.8%, respectively. Mortality models substantially outperformed readmission models (best admission-model AUC 0.821 for the Voting Ensemble vs. 0.645 for Logistic Regression in readmission; best discharge-model mortality AUC 0.828 for Logistic Regression, 95% CI: 0.694-0.930). Discharge models did not improve over admission models for either outcome. Key mortality predictors included code status, albumin level, functional dependence, and age. Conclusions: Admission-available clinical variables enabled good discrimination of 90-day mortality but only modest readmission prediction. Given the low events-per-variable ratio and single-centre design, these findings should be considered hypothesis-generating and require external validation before clinical implementation.