Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record
Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record
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DOI：
10.1136/amiajnl-2014-002642
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发表时间：
2015-04-01
影响因子：
6.4
通讯作者：
Savova, Guergana K.
中科院分区：
文献类型：
--
作者：
Lin, Chen;Karlson, Elizabeth W.;Savova, Guergana K.
目的通过增加时间特征自动识别类风湿关节炎(RA)合并甲氨蝶呤(MTA)转氨酶异常的患者，提高电子病历(EMR)结构化和非结构化成分挖掘的准确性。材料与方法将编码信息和字符串匹配算法应用于来自Partners Healthcare的5903名RA患者队列，选择1130名潜在肝毒性患者。有监督的机器学习被作为我们的主要方法。对于特征，使用APACHE临床文本分析和知识提取系统(CTAKES)从非结构化临床叙事的相关部分提取标准词汇。进一步提取时间特征以评估与转氨酶异常日期有关的事件提及的时间相关性。结果在训练集(N=480名患者)中在患者水平上进行总结，并对照测试集(N=120名患者)进行评估。结果该系统在测试集上的阳性预测值为0.756，敏感度为0.919，F1评分为0.829，明显优于最佳基线系统(PPV为0.590，敏感度为0.703，F1评分为0.642)。我们的创新，包括将表型问题框定为病例级分类任务，以及添加时间信息，都被证明是非常有效的。结论基于EMR中结构化和非结构化信息的RA患者甲氨蝶呤诱导的肝毒性表型自动发现具有准确的结果。我们的工作表明，添加时间特征显著改善了分类结果。
Objectives To improve the accuracy of mining structured and unstructured components of the electronic medical record (EMR) by adding temporal features to automatically identify patients with rheumatoid arthritis (RA) with methotrexate-induced liver transaminase abnormalities.Materials and methods Codified information and a string-matching algorithm were applied to a RA cohort of 5903 patients from Partners HealthCare to select 1130 patients with potential liver toxicity. Supervised machine learning was applied as our key method. For features, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) was used to extract standard vocabulary from relevant sections of the unstructured clinical narrative. Temporal features were further extracted to assess the temporal relevance of event mentions with regard to the date of transaminase abnormality. All features were encapsulated in a 3-month-long episode for classification.Results were summarized at patient level in a training set (N=480 patients) and evaluated against a test set (N=120 patients). Results The system achieved positive predictive value (PPV) 0.756, sensitivity 0.919, F1 score 0.829 on the test set, which was significantly better than the best baseline system (PPV 0.590, sensitivity 0.703, F1 score 0.642). Our innovations, which included framing the phenotype problem as an episode-level classification task, and adding temporal information, all proved highly effective.Conclusions Automated methotrexate-induced liver toxicity phenotype discovery for patients with RA based on structured and unstructured information in the EMR shows accurate results. Our work demonstrates that adding temporal features significantly improved classification results.