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

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A Commentary to this article was published on 08 August 2017

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.

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Acknowledgements

We would like to thank Philip Silberman and Dan Schneider of the Northwestern Medicine Enterprise Data Warehouse for help with data management.

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Correspondence to Yuan Luo.

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This work is funded by a Grant from the Pharmacovigilance and Patient Safety department at AbbVie, Inc.

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Yuan Luo, William Thompson, Timothy Herr, Zexian Zeng, Mark Berendsen, Siddhartha Jonnalagadda, Matthew Carson, and Justin Starren have no conflicts of interest that are directly relevant to the content of this study.

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Luo, Y., Thompson, W.K., Herr, T.M. et al. Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review. Drug Saf 40, 1075–1089 (2017). https://doi.org/10.1007/s40264-017-0558-6

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