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Prediction of Medical Concepts in Electronic Health Records: Similar Patient Analysis

机译：电子健康记录中医学概念预测：类似患者分析

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Background Medicine 2.0—the adoption of Web 2.0 technologies such as social networks in health care—creates the need for apps that can find other patients with similar experiences and health conditions based on a patient’s electronic health record (EHR). Concurrently, there is an increasing number of longitudinal EHR data sets with rich information, which are essential to fulfill this need. Objective This study aimed to evaluate the hypothesis that we can leverage similar EHRs to predict possible future medical concepts (eg, disorders) from a patient’s EHR. Methods We represented patients’ EHRs using time-based prefixes and suffixes, where each prefix or suffix is a set of medical concepts from a medical ontology. We compared the prefixes of other patients in the collection with the state of the current patient using various interpatient distance measures. The set of similar prefixes yields a set of suffixes, which we used to determine probable future concepts for the current patient’s EHR. Results We evaluated our methods on the Multiparameter Intelligent Monitoring in Intensive Care II data set of patients, where we achieved precision up to 56.1% and recall up to 69.5%. For a limited set of clinically interesting concepts, specifically a set of procedures, we found that 86.9% (353/406) of the true-positives are clinically useful, that is, these procedures were actually performed later on the patient, and only 4.7% (19/406) of true-positives were completely irrelevant. Conclusions These initial results indicate that predicting patients’ future medical concepts is feasible. Effectively predicting medical concepts can have several applications, such as managing resources in a hospital.
机译：背景医学2.0-使用Web 2.0技术（如医疗保健社交网络）的采用 - 为基于患者的电子健康记录（EHR）（EHR）而来，可以找到其他患者的应用程序。同时，越来越多的纵向EHR数据集，具有丰富的信息，这对于满足这种需求至关重要。目的本研究旨在评估我们可以利用类似EHR的假设来预测来自患者的EHR的可能的未来医学概念（例如，疾病）。方法使用基于时间的前缀和后缀表示患者的EHR，其中每个前缀或后缀是医疗本体的一组医学概念。我们将其他患者的前缀与当前患者的状态进行了使用各种液间距离措施。该组类似前缀产生了一组后缀，我们用于确定当前患者EHR的可能未来的概念。结果我们对患者密集护理II数据集的Multiparameter智能监测进行了评估了我们的方法，我们实现了高达56.1％的精确度，并记得高达69.5％。对于一套有限的临床有趣的概念，特别是一套程序，我们发现86.9％（353/406）的真阳性是临床上有用的，即，这些程序实际上是在患者稍后进行的，只有4.7真阳性的％（19/406）完全无关紧要。结论这些初步结果表明，预测患者未来的医学概念是可行的。有效预测医学概念可以有几个应用，例如管理医院的资源。

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