EPIDEMIOLOGY AND HEALTH DATA INSIGHTS
Review Article
Nov 03, 2025 48 20

Developing and Validating Predictive Models for Adverse Drug Reactions using Electronic Health Records (EHRs): A Narrative Review

Vivian Ukamaka Nwokedi 1 * ,
Michael Kayode Odedele 2,
Taofeek Adeshina Yusuff 3,
Irene Adjoa Anderson 4,
Olukunle O. Akanbi 5,
Chiamaka Pamela Agu 6,
Amber Otibhor Omoike 7
1 Department of Clinical Pharmacy, Faculty of Pharmacy, University of Benin, Benin City, Nigeria2 Department of Kinesiology, College of Health, Human Services & Nursing, California State University, Dominguez Hills, Carson, CA, USA3 Department of Business Analytics, Pompea College of Business, University of New Haven, West Haven, CT, USA4 Department of Pharmacology, Faculty of pharmacy and pharmaceutical sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana5 Department of Psychology and Behavioral Sciences, National Louis University, Tampa, FL, USA6 Department of Public Health, University of New Haven, West Haven, CT, USA7 Department of Pharmacy, Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria* Corresponding Author
Epidemiology and Health Data Insights, 1(6), 2025, ehdi019, https://doi.org/10.63946/ehdi/17369
Publication date: Nov 03, 2025
Full Text (PDF)

ABSTRACT

Adverse drug reactions (ADRs) remain a major global challenge, contributing substantially to patient morbidity, mortality, and healthcare costs. Traditional pharmacovigilance approaches—spontaneous reporting and post-marketing surveillance—are hampered by underreporting, delays, and limited contextual data. The growing availability of electronic health records (EHRs), which capture longitudinal structured and unstructured patient information, presents an unprecedented opportunity to advance ADR prediction. This narrative review synthesizes recent progress in developing and validating predictive models that leverage EHRs, highlighting methodological approaches, challenges, and future directions. Predictive strategies range from traditional regression models to advanced machine learning and deep learning architectures, with multimodal frameworks increasingly integrating structured fields (demographics, labs, prescriptions) and unstructured clinical text through natural language processing. While ensemble and deep learning methods demonstrate superior performance, issues of data quality, missingness, bias, and interpretability persist. Robust validation frameworks—spanning internal cross-validation to multi-center external testing—are critical to ensure generalizability and clinical trustworthiness. Ethical considerations, including fairness, privacy, and transparency, remain central to safe deployment. Looking forward, promising avenues include federated learning across institutions, integration of multi-omics and pharmacogenomic data, explainable AI tailored for clinical use, and real-time monitoring through digital twin frameworks. These trajectories, combined with robust governance and clinician–data scientist collaboration, have the potential to transform ADR detection from a reactive process to proactive, personalized prevention. By synthesizing the existing evidence, this review provides insights into the development of more effective predictive models for ADRs and informs strategies for improving pharmacovigilance. This study will contribute to the ongoing efforts to leverage EHRs and predictive models for improving patient outcomes and reducing the burden of ADRs.

KEYWORDS

Adverse Drug Reactions (ADRs) Electronic Health Records (EHRs) Predictive Model-ing Machine Learning Pharmacovigilance Artificial Intelligence (AI)

CITATION (Vancouver)

Nwokedi VU, Odedele MK, Yusuff TA, Anderson IA, Akanbi OO, Agu CP, et al. Developing and Validating Predictive Models for Adverse Drug Reactions using Electronic Health Records (EHRs): A Narrative Review. Epidemiology and Health Data Insights. 2025;1(6):ehdi019. https://doi.org/10.63946/ehdi/17369
APA
Nwokedi, V. U., Odedele, M. K., Yusuff, T. A., Anderson, I. A., Akanbi, O. O., Agu, C. P., & Omoike, A. O. (2025). Developing and Validating Predictive Models for Adverse Drug Reactions using Electronic Health Records (EHRs): A Narrative Review. Epidemiology and Health Data Insights, 1(6), ehdi019. https://doi.org/10.63946/ehdi/17369
Harvard
Nwokedi, V. U., Odedele, M. K., Yusuff, T. A., Anderson, I. A., Akanbi, O. O., Agu, C. P., and Omoike, A. O. (2025). Developing and Validating Predictive Models for Adverse Drug Reactions using Electronic Health Records (EHRs): A Narrative Review. Epidemiology and Health Data Insights, 1(6), ehdi019. https://doi.org/10.63946/ehdi/17369
AMA
Nwokedi VU, Odedele MK, Yusuff TA, et al. Developing and Validating Predictive Models for Adverse Drug Reactions using Electronic Health Records (EHRs): A Narrative Review. Epidemiology and Health Data Insights. 2025;1(6), ehdi019. https://doi.org/10.63946/ehdi/17369
Chicago
Nwokedi, Vivian Ukamaka, Michael Kayode Odedele, Taofeek Adeshina Yusuff, Irene Adjoa Anderson, Olukunle O. Akanbi, Chiamaka Pamela Agu, and Amber Otibhor Omoike. "Developing and Validating Predictive Models for Adverse Drug Reactions using Electronic Health Records (EHRs): A Narrative Review". Epidemiology and Health Data Insights 2025 1 no. 6 (2025): ehdi019. https://doi.org/10.63946/ehdi/17369
MLA
Nwokedi, Vivian Ukamaka et al. "Developing and Validating Predictive Models for Adverse Drug Reactions using Electronic Health Records (EHRs): A Narrative Review". Epidemiology and Health Data Insights, vol. 1, no. 6, 2025, ehdi019. https://doi.org/10.63946/ehdi/17369

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