Risk Prediction and Interpretation for Fall Events Using Explainable AI and Large Language Models

Falls represent a significant public health concern, particularly among older adults, with approximately 35.6 million cases annually in the United States alone. Traditional machine learning-based fall risk prediction methods often lack precision and interpretability, limiting their effectiveness in clinical settings. This study aims to develop and validate an explainable machine learning approach for fall risk prediction that combines predictive accuracy with interpretable results and leverages large language models for enhanced communication, enabling healthcare providers to make informed decisions about fall prevention strategies. We developed an integrated machine learning pipeline utilizing XGBoost classifier to process key health indicators such as age, and diagnosis history, with the model's interpretability enhanced through SHAP (SHapley Additive exPlanations) values. Results were transformed into natural language narratives using LangChain and large language models, which automated the generation of personalized reports combining both risk assessments and feature explanations, with patients classified into low (0-40%), medium (40-70%), and high (70-100%) risk groups based on predicted probabilities. The XG-Boost classifier emerged as the best-performing model, achieving 71% accuracy, 69% precision, 76% recall, an F1-score of 72%, and an ROC AUC of 0.71 on the testing dataset. The SHAP analysis provided transparent insights into feature importance, highlighting critical contributors to fall risk prediction. The integration of large language models through LangChain successfully transformed complex model outputs into comprehensible narratives, generating detailed explanations that included both high-level risk summaries and specific feature contributions for healthcare providers and patients. Our integrated approach demonstrates the feasibility of combining high-accuracy fall risk prediction with explainable AI techniques and large language models, with the system's ability to provide interpretable results and generate clear, personalized risk communications representing a significant advancement in developing practical AI-driven tools for clinical fall risk assessment, potentially improving the implementation of preventive interventions in healthcare settings.