HOSPITAL READMISSION
90% recall on readmissions, from a Gradient Boosting model tuned for the costly miss.
BRIEFING
Readmission risk scoring on 25,000 anonymized records. Three models were compared on ROC-AUC, Logistic Regression, Random Forest, and Gradient Boosting, and the Gradient Boosting champion (ROC-AUC 0.66) was threshold-tuned to catch 90% of readmissions. In a clinical setting a missed high-risk patient costs far more than a false alarm, so recall is the right target. The model's feature importances surfaced prior inpatient visits, medication count, and lab procedures as the dominant drivers, pointing discharge planning at the right patients.
ROLE / METHOD / OUTCOME
STACK: Python · scikit-learn · Gradient Boosting