Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records - A Scoping Review

Aug 5, 2026

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Natural language processing (NLP) and machine learning (ML) approaches can use unstructured free-text electronic health record (EHR) data to identify adverse drug events (ADEs) and strengthen pharmacovigilance, yet real-world effectiveness remains uncertain. This scoping review summarizes evidence on how NLP/ML detects ADEs from unstructured EHR sources and compares outcomes with other data sources. Studies were selected through searches of six databases in July 2023 and synthesized narratively, covering model types, ADE targets, performance, and pharmacovigilance impact. Findings suggest improved detection of under-reported events and safety signals, while variability and lack of standardised validation limit translation into routine practice.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Introduction

Electronic health records and unstructured data

Role of NLP/ML in pharmacovigilance

Methods

Scoping review design and study selection

Data extraction and synthesis

Results

Included studies and techniques

Model performance and impacts

Conclusions

This is a repository copy of Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records:A Scoping Review. White Rose Research Online URL for this paper: [https://eprints.whiterose.ac.uk/221636/](https://eprints.whiterose.ac.uk/221636/) Version: Published Version Article: Golder, Su [orcid.org/0000-0002-8987-5211](orcid.org/0000-0002-8987-5211) , Xu, Dongfang, O'Connor, Karen et al. (3 more authors) (2025) Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records:A Scoping Review. DRUG SAFETY. ISSN 0114-5916 [https://doi.org/10.1007/s40264-024-01505-6](https://doi.org/10.1007/s40264-024-01505-6) Reuse This article is distributed under the terms of the Creative Commons Attribution-NonCommercial (CC BY-NC) licence. This licence allows you to remix, tweak, and build upon this work non-commercially, and any new works must also acknowledge the authors and be non-commercial. You don’t have to license any derivative works on the same terms. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/) Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request. [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/) Drug Safety [https://doi.org/10.1007/s40264-024-01505-6](https://doi.org/10.1007/s40264-024-01505-6) Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/ Medical Records: A Scoping Review Su Golder1 · Dongfang Xu2 · Karen O’Connor3 · Yunwen Wang4 · Mahak Batra1 · Graciela Gonzalez Hernandez2 Accepted: 24 November 2024 © The Author(s) 2025 Abstract Background Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear. Objective To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources. Methods A scoping review was conducted by searching six databases in July 2023. Studies leveraging NLP/ML to identify ADEs from EHR were included. Titles/abstracts were screened by two independent researchers as were full-text articles. Data extraction was conducted by one researcher and checked by another. A narrative synthesis summarises the research techniques, ADEs analysed, model performance and pharmacovigilance impacts. Results Seven studies met the inclusion criteria covering a wide range of ADEs and medications. The utilisation of rulebased NLP, statistical models, and deep learning approaches was observed. Natural language processing/ML techniques with unstructured data improved the detection of under-reported adverse events and safety signals. However, substantial variability was noted in the techniques and evaluation methods employed across the different studies and limitations exist in integrating the findings into practice. Conclusions Natural language processing (NLP) and machine learning (ML) have promising possibilities in extracting valuable insights with regard to pharmacovigilance from unstructured EHR data. These approaches have demonstrated proficiency in identifying specific adverse events and uncovering previously unknown safety signals that would not have been apparent through structured data alone. Nevertheless, challenges such as the absence of standardised met

FAQs

What is the purpose of this scoping review?

It summarises evidence on how NLP/ML methods detect adverse drug events from unstructured EHR data, and considers implications for improving pharmacovigilance compared with other data sources.

How were the studies selected and analysed?

A scoping review searched six databases in July 2023; titles/abstracts and full texts were screened by independent researchers, and data were extracted by one researcher and checked by another, followed by narrative synthesis.

What do the results indicate about NLP/ML for ADE detection?

NLP/ML using unstructured data improved detection of under-reported adverse events and helped uncover safety signals, with rule-based NLP, statistical models, and deep learning approaches reported across included studies.

What challenges limit adopting these methods in practice?

Substantial variability in techniques and evaluation methods, along with the absence of standardised methodologies and validation criteria, obstructs widespread adoption of NLP/ML for pharmacovigilance using unstructured EHR data.

Aug 5, 2026

Upload by Liam