A context-aware approach for progression tracking of medical concepts in electronic medical records
A context-aware approach for progression tracking of medical concepts in electronic medical records
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DOI：
10.1016/j.jbi.2015.09.013
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发表时间：
2015-12-01
影响因子：
4.5
通讯作者：
Hsu, Wen-Lian
中科院分区：
文献类型：
--
作者：
Chang, Nai-Wen;Dai, Hong-Jie;Hsu, Wen-Lian
糖尿病患者的电子病历（EMR）包含有关心脏病风险因素的信息，如高血压，胆固醇水平和吸烟状况。发现所描述的风险因素并跟踪其随时间的进展可以支持医务人员做出临床决策，并促进数据建模和生物医学研究。这种高度患者特异性的知识对于推动循证实践的发展至关重要，也有助于改善个性化医疗和护理。跟踪EMR中描述的疾病进展及其风险因素的一种通用方法是首先识别所有时间表达，然后将它们中的每一个分配给最近的目标医学概念。然而，此方法可能并不总是提供正确的关联。鉴于此，这项工作引入了一个上下文感知的方法来分配的时间属性的识别风险因素，通过重建上下文，包含更可靠的时间表达式。i2b2测试集上的评估结果证明了所提出方法的有效性，其F分数为0.897。为了提高该方法处理非结构化临床文本的能力，并允许再现演示结果，可在https://sites.google.com/site/hongjiedai/projects/nttmuclinicalnet上获得一组用于开发该系统的已开发.NET库。(C)2015 Elsevier Inc. All rights reserved.
Electronic medical records (EMRs) for diabetic patients contain information about heart disease risk factors such as high blood pressure, cholesterol levels, and smoking status. Discovering the described risk factors and tracking their progression over time may support medical personnel in making clinical decisions, as well as facilitate data modeling and biomedical research. Such highly patient-specific knowledge is essential to driving the advancement of evidence-based practice, and can also help improve personalized medicine and care. One general approach for tracking the progression of diseases and their risk factors described in EMRs is to first recognize all temporal expressions, and then assign each of them to the nearest target medical concept. However, this method may not always provide the correct associations. In light of this, this work introduces a context-aware approach to assign the time attributes of the recognized risk factors by reconstructing contexts that contain more reliable temporal expressions. The evaluation results on the i2b2 test set demonstrate the efficacy of the proposed approach, which achieved an F-score of 0.897. To boost the approach's ability to process unstructured clinical text and to allow for the reproduction of the demonstrated results, a set of developed .NET libraries used to develop the system is available at https://sites.google.com/site/hongjiedai/projects/nttmuclinicalnet. (C) 2015 Elsevier Inc. All rights reserved.