Automated extraction of clinical traits of multiple sclerosis in electronic medical records.
Automated extraction of clinical traits of multiple sclerosis in electronic medical records.
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DOI：
10.1136/amiajnl-2013-001999
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发表时间：
2013-12
期刊：
影响因子：
--
通讯作者：
Haines JL
中科院分区：
文献类型：
--
作者：
Davis MF;Sriram S;Bush WS;Denny JC;Haines JL
多发性硬化症（MS）的临床过程是高度可变的，研究数据收集是昂贵和耗时的。我们评估了应用于电子病历（EMR）的自然语言处理技术，以识别MS患者及其病程的关键临床特征。我们使用了四种基于ICD-9代码、文本关键词和药物的算法，从范德比尔特大学的EMR的去识别化研究版本中识别MS患者。使用899个个体的记录的训练数据集，构建算法以从医疗记录的文本中识别和提取关于MS临床过程的详细信息，包括临床亚型、寡克隆条带的存在、诊断年份、首次症状的年份和起源、扩展残疾状态量表（EDSS）评分、定时25英尺步行评分和MS药物。算法在由两名独立评审员验证的测试集上进行评估。我们确定了5789人MS。所有临床特征提取，精确度至少为87%，特异性大于80%。临床亚型、EDSS评分和定时25英尺步行评分的回忆值均大于80%。该临床数据集代表了可用于MS研究的最大的详细临床特征数据库之一。这项工作表明，详细的临床信息记录在EMR中，并且可以以高可靠性提取用于研究目的。
The clinical course of multiple sclerosis (MS) is highly variable, and research data collection is costly and time consuming. We evaluated natural language processing techniques applied to electronic medical records (EMR) to identify MS patients and the key clinical traits of their disease course. We used four algorithms based on ICD-9 codes, text keywords, and medications to identify individuals with MS from a de-identified, research version of the EMR at Vanderbilt University. Using a training dataset of the records of 899 individuals, algorithms were constructed to identify and extract detailed information regarding the clinical course of MS from the text of the medical records, including clinical subtype, presence of oligoclonal bands, year of diagnosis, year and origin of first symptom, Expanded Disability Status Scale (EDSS) scores, timed 25-foot walk scores, and MS medications. Algorithms were evaluated on a test set validated by two independent reviewers. We identified 5789 individuals with MS. For all clinical traits extracted, precision was at least 87% and specificity was greater than 80%. Recall values for clinical subtype, EDSS scores, and timed 25-foot walk scores were greater than 80%. This collection of clinical data represents one of the largest databases of detailed, clinical traits available for research on MS. This work demonstrates that detailed clinical information is recorded in the EMR and can be extracted for research purposes with high reliability.
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影响因子：
11.2
作者：
POSER, CM;PATY, DW;TOURTELLOTTE, WW
通讯作者：
TOURTELLOTTE, WW
影响因子：
11.2
作者：
McDonald, WI;Compston, A;Wolinsky, JS
通讯作者：
Wolinsky, JS
影响因子：
6.7
作者：
LePendu, P.;Iyer, S. V.;Bauer-Mehren, A.;Harpaz, R.;Mortensen, J. M.;Podchiyska, T.;Ferris, T. A.;Shah, N. H.
通讯作者：
Shah, N. H.
影响因子：
11.2
作者：
Polman CH;Reingold SC;Banwell B;Clanet M;Cohen JA;Filippi M;Fujihara K;Havrdova E;Hutchinson M;Kappos L;Lublin FD;Montalban X;O'Connor P;Sandberg-Wollheim M;Thompson AJ;Waubant E;Weinshenker B;Wolinsky JS
通讯作者：
Wolinsky JS
影响因子：
5.8
作者：
Fischer, JS;Rudick, RA;Reingold, SC
通讯作者：
Reingold, SC