Shah Setu, Luo Xiao, Kanakasabai Saravanan, Tuason Ricardo, Klopper Gregory
1Purdue School of Engineering and Technology, IUPUI, 799 W. Michigan Street, Indianapolis, IN 46202 USA.
2Indiana University Health South Campus, 1515 N. Senate Ave, Indianapolis, IN 46202 USA.
Health Inf Sci Syst. 2018 Nov 28;7(1):1. doi: 10.1007/s13755-018-0062-0. eCollection 2019 Dec.
There are challenges for analyzing the narrative clinical notes in Electronic Health Records (EHRs) because of their unstructured nature. Mining the associations between the clinical concepts within the clinical notes can support physicians in making decisions, and provide researchers evidence about disease development and treatment. In this paper, in order to model and analyze disease and symptom relationships in the clinical notes, we present a concept association mining framework that is based on word embedding learned through neural networks. The approach is tested using 154,738 clinical notes from 500 patients, which are extracted from the Indiana University Health's Electronic Health Records system. All patients are diagnosed with more than one type of disease. The results show that this concept association mining framework can identify related diseases and symptoms. We also propose a method to visualize a patients' diseases and related symptoms in chronological order. This visualization can provide physicians an overview of the medical history of a patient and support decision making. The presented approach can also be expanded to analyze the associations of other clinical concepts, such as social history, family history, medications, etc.
由于电子健康记录（EHR）中的叙述性临床记录具有非结构化的性质，对其进行分析存在挑战。挖掘临床记录中临床概念之间的关联可以帮助医生做出决策，并为研究人员提供有关疾病发展和治疗的证据。在本文中，为了对临床记录中的疾病和症状关系进行建模和分析，我们提出了一个基于通过神经网络学习的词嵌入的概念关联挖掘框架。该方法使用从印第安纳大学健康系统的电子健康记录系统中提取的500名患者的154,738份临床记录进行了测试。所有患者都被诊断出患有不止一种疾病。结果表明，这个概念关联挖掘框架可以识别相关的疾病和症状。我们还提出了一种方法，按时间顺序可视化患者的疾病和相关症状。这种可视化可以为医生提供患者病史的概述，并支持决策制定。所提出的方法还可以扩展到分析其他临床概念的关联，如社会史、家族史、药物治疗等。
