An Explainable AI Data Pipeline for Multi-Level Survival Prediction of Breast Cancer Patients Using Electronic Medical Records and Social Determinants of Health Data

Abstract

This study introduces an innovative explainable AI (XAI) pipeline designed to predict breast cancer survival by integrating clinical, socioeconomic, and geographic data. Using data from 10,172 patients treated at hospitals in the Memphis, Tennessee metropolitan area, the pipeline identifies key survival determinants and reveals significant survival disparities affecting Black women. Advanced machine learning models combined with SHapley Additive exPlanations (SHAP) provide actionable and interpretable insights into the role of tumor stage, socioeconomic conditions, and access to preventive care. This framework facilitates personalized survival predictions and targeted equity-focused interventions, demonstrating the potential of multi-source data integration to address health inequities and improve patient outcomes.

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