Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure Prediction
Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure Prediction
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发表时间：
2016-02
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通讯作者：
E. Choi;A. Schuetz;W. Stewart;Jimeng Sun
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作者：
E. Choi;A. Schuetz;W. Stewart;Jimeng Sun
目的：使用数据驱动方法将电子健康记录中的异质临床数据转换为具有临床意义的构造特征，该方法部分依赖于数据之间的时间关系。材料和方法：对医学概念和患者的临床有意义的表述是健康分析应用的关键。现有的大多数方法直接构造映射到原始数据的特征(例如，ICD或CPT代码)，或者利用一些本体映射，例如SNOMED代码。然而，现有的方法中没有一种直接利用EHR数据来学习这种概念表示。我们提出了一种新的方法来表示不同的医学概念(例如，诊断、药物和手术)，该方法基于纵向电子健康记录中的共现模式。该方法背后的直觉是将在时间上密切共存的医学概念映射到类似的概念向量，以便它们之间的距离将较小。我们还推导出了一种从相关的医学概念向量构造患者向量的简单方法。结果：我们通过诊断、用药和程序评估相似的医学概念。结果表明，相似的医学概念对之间存在xx%的相关性。我们提出的表示法显著提高了心力衰竭(HF)的预测建模性能，其中分类方法(如Logistic回归、神经网络、支持向量机和K近邻)使用该表示法可使ROC曲线下面积(AUC)提高高达23%。结论：提出了一种有效的患者和医学概念表征学习方法。由此得到的表示可以将相关概念映射在一起，并且还提高了预测建模的性能。
Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data. Materials and Methods: The clinically meaningful representations of medical concepts and patients are the key for health analytic applications. Most of existing approaches directly construct features mapped to raw data (e.g., ICD or CPT codes), or utilize some ontology mapping such as SNOMED codes. However, none of the existing approaches leverage EHR data directly for learning such concept representation. We propose a new way to represent heterogeneous medical concepts (e.g., diagnoses, medications and procedures) based on co-occurrence patterns in longitudinal electronic health records. The intuition behind the method is to map medical concepts that are co-occuring closely in time to similar concept vectors so that their distance will be small. We also derive a simple method to construct patient vectors from the related medical concept vectors. Results: We evaluate similar medical concepts across diagnosis, medication and procedure. The results show xx% relevancy between similar pairs of medical concepts. Our proposed representation significantly improves the predictive modeling performance for onset of heart failure (HF), where classification methods (e.g. logistic regression, neural network, support vector machine and K-nearest neighbors) achieve up to 23% improvement in area under the ROC curve (AUC) using this proposed representation. Conclusion: We proposed an effective method for patient and medical concept representation learning. The resulting representation can map relevant concepts together and also improves predictive modeling performance.