Supervised Machine Learning-Based Prediction of In-Hospital Mortality Following Hip Fracture in Older Adults

Abstract

Hip fractures in older adults are associated with substantial morbidity, functional decline, and high in-hospital mortality. Early identification of patients at increased risk of death may improve clinical decision-making and resource allocation. This study aimed to develop and internally validate supervised machine learning models to predict in-hospital mortality among older adults hospitalized for hip fracture using nationwide administrative data from Chile. A retrospective cohort study was conducted using anonymized hospital discharge records from the Chilean National Health Fund (FONASA), covering admissions between 1 January 2019 and 31 December 2024, across 72 public hospitals. Demographic, clinical, and care-related variables were included as predictors. Multiple supervised machine learning algorithms were trained and evaluated using stratified train–test partitioning. Model performance was assessed using AUC-ROC, precision, recall, and F1-score. Model interpretability was explored using SHapley Additive exPlanations (SHAP). A total of 40,253 hospitalization episodes were analyzed. The Gradient Boosting model achieved the best overall performance, with an AUC-ROC of 0.885 and a favorable balance between precision and recall. SHAP analysis identified age, comorbidity burden, and surgical treatment as the most influential predictors, revealing nonlinear and clinically meaningful contributions to mortality risk. Supervised machine learning models based on routinely collected administrative data demonstrated strong predictive performance for in-hospital mortality after hip fracture. Interpretable models may support early risk stratification and clinical decisionmaking at a national healthcare level.