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

Neda Aminnejad¹, Emmalin Buajitti^2,3, Laura C Rosella^2,3

¹Department of Mathematics and Statistics, York University, Toronto, ON, Canada. neda2727@yorku.ca.

Journal of Medical Systems

|October 14, 2025

Summary

This study developed a machine learning model to predict all-cause mortality in diabetic patients two years in advance. The model uses common health data and achieves high accuracy, improving risk identification for better patient management.

Keywords:

Diabetes mellitus EHR data Machine learning Mortality XGBoost

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Area of Science:

Healthcare Informatics
Machine Learning in Medicine
Predictive Analytics

Background:

Diabetic patients face elevated all-cause mortality risks.
Existing mortality prediction models often lack generalizability or rely on limited variables.
Accurate, early prediction of mortality is crucial for proactive patient management in diabetes care.

Purpose of the Study:

To develop and validate a machine learning model for predicting all-cause mortality in diabetic patients.
To leverage comprehensive administrative health data for enhanced predictive accuracy.
To identify key risk factors associated with mortality in this population.

Main Methods:

Utilized XGBoost algorithm on a dataset of 1553 variables from administrative health records.
Included hospitalization, emergency department visits, demographics, and chronic disease information.
Evaluated model performance using Area Under the Curve (AUC).

Main Results:

Achieved an AUC of 0.89, outperforming existing models.
Identified significant predictors: age, immigration status, comorbidity diagnosis age, number, and duration.
Demonstrated robust discrimination and calibration using commonly available primary care data.

Conclusions:

The machine learning model effectively predicts two-year all-cause mortality in diabetic patients.
The model's reliance on accessible data facilitates broad clinical application.
Findings support improved patient management and resource allocation through early risk stratification.