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Is the inter-patient coincidence of a subclinical disorder related to EHR similarity?

机译:亚临床疾病的患者间符合是否与EHR相似性相关?

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Electronic Health Record (EHR) provide clinical evidence for identifying subclinical diseases and supporting decisions on early intervention. Simple string matching cannot link up the conceptually similar but verbally different clinical terms in patient records, limiting the usefulness of EHR. A novel ontological similarity matching approach supported by the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) is proposed in this paper. The disease terms of a patient record are transformed into a vector space so that each patient record can be characterized by a feature vector. The similarity between the new record and an existing database record was quantified by a kernel function of their feature vectors. The matches are ranked by their similarity scores. To evaluate the proposed matching approach, medical history and carotid ultrasonic imaging finding were collected from 47 subjects in Hong Kong. The dataset formed 1081 pairs of patient records and the ROC analysis was used to evaluate and compare the accuracy of the ontological similarity matching and the simple string matching against the presence or absence of carotid plaques identified in ultrasound examination. It was found that the simple string matching randomly rated the record pairs but the ontological similarity matching provided non-random rating.
机译:电子健康记录(EHR)为识别亚临床疾病和支持早期干预决策提供了临床证据。简单的字符串匹配无法将患者记录中概念上相似但语言上不同的临床术语联系起来,从而限制了EHR的实用性。本文提出了一种新的本体相似度匹配方法,该方法由“医学术语临床术语系统化”(SNOMED-CT)支持。将患者记录的疾病术语转换到向量空间中,以便每个患者记录都可以通过特征向量来表征。新记录和现有数据库记录之间的相似性通过其特征向量的核函数来量化。比赛按其相似性分数排名。为了评估建议的匹配方法,从香港的47位受试者中收集了病史和颈动脉超声影像学发现。该数据集形成了1081对患者记录,并且使用ROC分析来评估和比较本体相似度匹配和简单字符串匹配与超声检查中识别出的颈动脉斑块存在与否的准确性。发现简单字符串匹配对记录对进行了随机评估,而本体相似度匹配则提供了非随机评估。

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