Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 750 North Lake Shore Drive, 11th floor, Chicago, IL, 60611, USA.
Galter Health Sciences Library, Northwestern University Feinberg School of Medicine, 750 North Lake Shore Drive, 11th floor, Chicago, IL, 60611, USA.
Drug Saf. 2017 Nov;40(11):1075-1089. doi: 10.1007/s40264-017-0558-6.
The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions. The ability to accurately capture both semantic and syntactic structures in clinical narratives becomes increasingly critical to enable efficient and accurate ADE detection. Significant progress has been made in algorithm development and resource construction since 2000. Since 2012, statistical analysis and machine learning methods have gained traction in automation of ADE mining from EHR narratives. Current state-of-the-art methods for NLP-based ADE detection from EHRs show promise regarding their integration into production pharmacovigilance systems. In addition, integrating multifaceted, heterogeneous data sources has shown promise in improving ADE detection and has become increasingly adopted. On the other hand, challenges and opportunities remain across the frontier of NLP application to EHR-based pharmacovigilance, including proper characterization of ADE context, differentiation between off- and on-label drug-use ADEs, recognition of the importance of polypharmacy-induced ADEs, better integration of heterogeneous data sources, creation of shared corpora, and organization of shared-task challenges to advance the state-of-the-art.
药物警戒的目标是通过药品检测、监测、描述和预防药物不良反应（ADE）。本文对将自然语言处理（NLP）应用于电子健康记录（EHR）叙述以进行药物警戒的最新进展进行了全面的结构化回顾。我们回顾了不同复杂度和问题重点的方法，总结了方法进展的最新现状，讨论了局限性，并指出了几个有前途的未来方向。准确捕捉临床叙述中的语义和句法结构的能力对于实现高效准确的 ADE 检测变得越来越重要。自 2000 年以来，在算法开发和资源建设方面取得了重大进展。自 2012 年以来，统计分析和机器学习方法在从 EHR 叙述中自动化 ADE 挖掘方面得到了关注。基于 NLP 的 EHR 中 ADE 检测的最新方法在将其集成到生产药物警戒系统方面显示出了前景。此外，整合多方面、异构数据源在提高 ADE 检测方面显示出了前景，并得到了越来越多的采用。另一方面，在将 NLP 应用于基于 EHR 的药物警戒的前沿仍然存在挑战和机遇，包括正确描述 ADE 背景、区分标签内和标签外药物使用的 ADE、认识到多药治疗引起的 ADE 的重要性、更好地整合异构数据源、创建共享语料库以及组织共享任务挑战以推进现状。
