Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review

Yuan Luo (), William K. Thompson, Timothy M. Herr, Zexian Zeng, Mark A. Berendsen, Siddhartha R. Jonnalagadda, Matthew B. Carson and Justin Starren
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Yuan Luo: Northwestern University Feinberg School of Medicine
William K. Thompson: Northwestern University Feinberg School of Medicine
Timothy M. Herr: Northwestern University Feinberg School of Medicine
Zexian Zeng: Northwestern University Feinberg School of Medicine
Mark A. Berendsen: Galter Health Sciences Library, Northwestern University Feinberg School of Medicine
Siddhartha R. Jonnalagadda: Northwestern University Feinberg School of Medicine
Matthew B. Carson: Northwestern University Feinberg School of Medicine
Justin Starren: Northwestern University Feinberg School of Medicine

Drug Safety, 2017, vol. 40, issue 11, No 3, 1075-1089

Abstract: Abstract 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.

Date: 2017
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DOI: 10.1007/s40264-017-0558-6

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