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

作者：S. Golder, Dongfang Xu, K. O’Connor, Yunwen Wang, Mahak Batra, Graciela Gonzalez Hernandez · 发表于：Drug Safety · 年份：2025 · DOI：10.1007/s40264-024-01505-6 · 被引用次数：59 · 研究领域：Medicine

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. 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. 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. Seven studies met the inclusion criteria covering a wide range of ADEs and medications. The utilisation of rule-based 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. Natural language processing (NLP) and machine learning (ML) have promising possibilities in ex...