Validating a natural language processing tool to exclude psychogenic nonepileptic seizures in electronic medical record-based epilepsy research
Validating a natural language processing tool to exclude psychogenic nonepileptic seizures in electronic medical record-based epilepsy research
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DOI：
10.1016/j.yebeh.2013.09.025
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发表时间：
2013-12-01
影响因子：
2.6
通讯作者：
Brandt, C. A.
中科院分区：
文献类型：
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作者：
Hamid, H.;Fodeh, S. J.;Brandt, C. A.
基本原理：随着电子健康记录（EHR）系统变得越来越可用，它们将成为癫痫研究中收集流行病学数据的重要资源。然而，由于临床医生没有一个系统的方法来编码心因性非癫痫发作（PNES），PNES患者往往被误分类为癫痫，导致抽样误差。本研究验证了一个自然语言处理（NLP）工具，使用语言信息，以帮助识别患者PNES.Methods：使用VA国家临床数据库，2200注伊拉克和阿富汗退伍军人谁完成视频脑电图（VEEG）监测手动审查，和退伍军人被确定为有记录PNES或没有。评审员确定了PNES相关词汇，以告知称为Yale cTakes Extension（YTEX）的NLP工具。使用NLP技术，YTEX在EHR中注释语法结构、命名实体及其否定上下文。这些注释被传递到分类器以检测没有PNES的患者。通过计算阳性预测值（PPV），灵敏度和F-score.Results：742名伊拉克和阿富汗退伍军人谁收到了诊断癫痫或癫痫发作障碍VEEG，44记录事件VEEG的分类进行了评估：22名退伍军人（3.0%）仅明确的PNES，20名（2.7%）可能有PNES，2名（0.3%）有PNES和癫痫记录。其余698名退伍军人在VEEG入院期间没有捕获事件和/或没有明确的诊断。结论：YTEX NLP工具和分类器在排除VEEG诊断的PNES方面具有较高的准确性。该工具可能是非常有价值的，以防止假阳性识别癫痫患者的EHR为基础的流行病学研究。(C)2013年由Elsevier Inc.出版
Rationale: As electronic health record (EHR) systems become more available, they will serve as an important resource for collecting epidemiologic data in epilepsy research. However, since clinicians do not have a systematic method for coding psychogenic nonepileptic seizures (PNES), patients with PNES are often misclassified as having epilepsy, leading to sampling error. This study validates a natural language processing (NLP) tool that uses linguistic information to help identify patients with PNES.Methods: Using the VA national clinical database, 2200 notes of Iraq and Afghanistan veterans who completed video electroencephalograph (VEEG) monitoring were reviewed manually, and the veterans were identified as having documented PNES or not. Reviewers identified PNES-related vocabulary to inform a NLP tool called Yale cTakes Extension (YTEX). Using NLP techniques, YTEX annotates syntactic constructs, named entities, and their negation context in the EHR. These annotations are passed to a classifier to detect patients without PNES. The classifier was evaluated by calculating positive predictive values (PPVs), sensitivity, and F-score.Results: Of the 742 Iraq and Afghanistan veterans who received a diagnosis of epilepsy or seizure disorder by VEEG, 44 had documented events on VEEG: 22 veterans (3.0%) had definite PNES only, 20 (2.7%) had probable PNES, and 2 (0.3%) had both PNES and epilepsy documented. The remaining 698 veterans did not have events captured during the VEEG admission and/or did not have a definitive diagnosis. Our classifier achieved a PPV of 93%, a sensitivity of 99%, and a F-score of 96%.Conclusion: Our study demonstrates that the YTEX NLP tool and classifier is highly accurate in excluding PNES, diagnosed with VEEG, in EHR systems. The tool may be very valuable in preventing false positive identification of patients with epilepsy in EHR-based epidemiologic research. (C) 2013 Published by Elsevier Inc.