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Real-World Pharmacotherapy-Driven Cardiovascular Risk Prediction Using Interpretable Machine Learning and Jordanian
Said Moshawih1, Lobna Gharaibeh2, Islam Alfreahat3
1Department of Pharmaceutical Sciences, Faculty of Pharmacy, Al-Ahliyya Amman University, Amman 19111, Jordan.
Insights
A new machine learning model, JoRisk, accurately predicts cardiovascular disease risk using electronic health records and medication data. This interpretable tool improves risk stratification in resource-limited settings.
Area of Science:
- Machine learning applications in healthcare
- Cardiovascular disease risk prediction
- Health informatics
Background:
- Cardiovascular disease (CVD) is a leading global cause of death, particularly in low- and middle-income countries.
- Existing risk models often lack accuracy and generalizability in these regions.
- Electronic health records (EHRs) offer a rich data source for improving risk prediction.
Purpose of the Study:
- To develop an interpretable machine learning model for cardiovascular risk prediction.
- To integrate pharmacotherapy data into cardiovascular risk assessment.
- To utilize national EHR data from Jordan for model development.
Main Methods:
- Retrospective cohort study of ~600,000 individuals from the Hakeem EHR system (2018-2022).
- Integration of demographic, clinical, blood pressure, laboratory, and medication data.
- Benchmarking, optimization, and calibration of machine learning models, including SHAP analysis for interpretability.
Main Results:
- High prevalence of hypertension (50.2%), hyperlipidemia (54.9%), and diabetes (47.9%) observed.
- The JoRisk model, a calibrated seed-bagged gradient boosting model, achieved strong performance (ROC-AUC 0.844).
- Key predictors included antihyperlipidemic therapy, blood pressure variability, age, and sex.
Conclusions:
- JoRisk provides a calibrated, interpretable machine learning framework for short-term cardiovascular risk prediction.
- The model effectively uses routinely available EHR variables and pharmacotherapy data.
- JoRisk serves as a scalable decision-support tool for risk stratification in resource-constrained healthcare systems.
Abstract:
Background: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with over 75% of deaths occurring in low- and middle-income countries, where conventional risk models often demonstrate poor calibration and limited generalizability. Objective: This study aimed to develop an interpretable, pharmacotherapy-informed machine learning model for cardiovascular risk prediction using national electronic health record (EHR) data from Jordan. Methods: A retrospective cohort study was conducted using approximately 600,000 individuals from the national Hakeem EHR system (2018-2022). Demographic, clinical, blood pressure, laboratory, and medication data were integrated to construct three datasets reflecting varying levels of feature completeness. Multiple machine learning models were benchmarked, followed by optimization, hybrid modeling, and probability calibration. Model interpretability was assessed using SHAP analysis. Results: The national cohort demonstrated a high cardiometabolic burden, with prevalence of hypertension (50.2%), hyperlipidemia (54.9%), and diabetes (47.9%). Antihypertensive and lipid-lowering therapies were more frequently used among CVD patients (56.9% and 49.6%, respectively). Treatment patterns were dominated by amlodipine (19.9%) and atorvastatin (74.4%). The final calibrated seed-bagged gradient boosting model achieved robust performance (ROC-AUC 0.844; PR-AUC 0.813) with consistent generalization across datasets. Key predictors included antihyperlipidemic therapy, systolic blood pressure variability, age, and sex. Conclusions: This study presents JoRisk, a calibrated and interpretable machine learning framework that integrates pharmacotherapy and clinical data for short-term cardiovascular risk prediction. The model demonstrates strong performance using routinely available EHR variables and offers a scalable decision-support tool for risk stratification in resource-constrained healthcare systems.
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