Predicting All-Cause Mortality in Diabetic Patients 2 Years in Advance Using Aggregated EHR Data and Machine Learning

This study presents a machine learning-driven model predicting all-cause mortality two years in advance using administrative health data focused on diabetic patients. Integrating hospitalization records, emergency department data, demographics, and chronic disease information for 1553 variables, the study utilizes XGBoost, achieving an AUC of 0.89, which comparatively surpasses existing models. The research emphasizes the machine learning model's efficacy in capturing intricate mortality risk relationships and highlighting risk factors. While prior models often relied on specific cohorts or limited variables, this model, based on commonly available variables in primary care data, displays robust discrimination and calibration. Additionally, it highlights significant predictors such as age, immigration status, diagnosis age of comorbidities, number of comorbidities, and durations of comorbidities, aiding in early risk identification. The study suggests a potential for enhanced patient management and resource allocation based on mortality risk predictions for diabetic populations, showcasing the impact of machine learning in healthcare.