Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and Electronic Health Records: A Feasibility Study
Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and Electronic Health Records: A Feasibility Study
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DOI：
10.1197/jamia.m3028
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发表时间：
2009-05-01
影响因子：
6.4
通讯作者：
Friedman, Carol
中科院分区：
文献类型：
--
作者：
Wang, Xiaoyan;Hripcsak, George;Friedman, Carol
目的：检测药物在整个市场生命周期内的全部安全性是至关重要的。然而，目前的药物警戒系统仍有很大的局限性。我们工作的目的是证明使用自然语言处理（NLP）、综合电子健康记录（EHR）和关联统计用于药物警戒目的的可行性。设计：叙述性出院摘要从纽约长老会医院（NYPH）的临床信息系统中收集。MedLEE是一个NLP系统，用于识别可能是潜在药物不良事件（ADEs）的用药事件和实体。使用共现统计和调整量检验来检测两类实体之间的关联，计算关联的强度，并确定它们的截止阈值。选取已知ade的7种药物/药物类别（布洛芬、吗啡、华法林、安非他酮、帕罗西汀、罗格列酮、ACE抑制剂）进行系统评价。结果：与7种药物相关的ade有132例。已知ade的总查全率和查准率分别为0.75和0.31。重要的是，使用历史回滚设计的定性评估表明，使用我们的系统可以检测到新的ade。结论：本研究为开发活性、高通量和前瞻性的系统提供了一个框架，这些系统有可能揭示药物在整个市场生命周期中的安全性。我们的结果表明，尽管存在一些具有挑战性的问题，但该框架是可行的。据我们所知，这是第一个使用电子病历中全面的非结构化数据进行药物警戒的研究。中华医学杂志，2009;16(1):328-337。DOI 10.1197 / jamia.M3028。
Objective: It is vital to detect the full safety profile of a drug throughtout its market life. current pharmacovigilance systems still have substantial limitations, however. The objective Of our work is to demonstrate the feasibility Of using natural language processing (NLP), the comprehensive Electronic Health Record (EHR), and association statistics for pharmacovigilance purposes.Design: Narrative discharge summaries were collected from the Clinical Information System at New York Presbyterian Hospital (NYPH). MedLEE, an NLP system, was applied to the collection to identify medication events and entities which could be potential adverse drug events (ADEs). co-occurrence statistics with adjusted volume tests were Used to detect associations between the two types of entities, to calculate the strengths of the associations, and to determine their cutoff thresholds. Seven drugs/drug classes (ibuprofen, morphine, warfarin, bupropion, paroxetine, rosiglitazone, ACE inhibitors) with known ADEs were selected to evaluate the system.Results: One hundred thirty-two potential ADEs were found to be associated with the 7 drugs. Overall recall and precision were 0.75 and 0.31 for known ADEs respectively. Importantly, qualitative evaluation Using historic roll back design suggested that novel ADEs could be detected using our system.Conclusions: This study provides a framework for the development of active, high-throughput and prospective systems which could potentially Unveil drug safety profile,, throughout their entire market life. Our results demonstrate that the framework is feasible although there are some challenging issues. To the best of our knowledge, this is the first study using comprehensive unstructured data from the EHR for pharmacovigilance. J Am Med Inform Assoc. 2009;16:328-337. DOI 10.1197/jamia.M3028.