Turn discharge data into clear readmission signals
A focused clinical AI demo that estimates 30-day readmission risk, compares model outputs, and explains what moved the score.
Inputs that explain the patient
From raw profile to interpretable action
Estimate risk quickly
Convert discharge parameters into a probability score between 0% and 100%.
Compare model behavior
Show XGBoost beside logistic regression so the prediction has a baseline.
Explain the drivers
Surface the features that push the prediction higher or lower.
Built to compare and explain
XGBoost
Captures nonlinear interactions between utilization, medications, and conditions.
Logistic Regression
Provides a simpler statistical baseline for comparison.
SHAP-style impact
Breaks the final score into directional feature contributions.
Educational use only
This demo should support, not replace, clinical judgment. Actual readmission risk depends on many factors not captured by the model, including social determinants of health, care coordination, adherence, and clinical assessment.