多源EHR数据的结构化集合矩阵分解表型分析
Phenotyping using Structured Collective Matrix Factorization of Multi--source EHR Data
摘要：
电子健康记录（EHR）的可用性越来越高，这推动了使用数据驱动方法的精准医疗倡议。这项工作的关键是能够从对EHR或基于EHR的表型的简单查询中识别出具有某些感兴趣的医疗条件的患者。现有的基于规则的表型分析方法是极其劳动密集型的。相反，来自机器学习的降维和潜在因素估计技术可以适用于没有（或最少）人类监督的表型提取。我们建议确定一个易于解释的潜在空间共享的各种来源的EHR数据作为潜在的候选表型。通过合并多个EHR数据源（例如，诊断、药物和实验室报告），我们的方法可以生成丰富的表型。此外，在表型分析应用中的容易解释性需要候选表型的稀疏表示，例如，源自患者的药物和诊断数据的每个表型应该优选地由少数诊断和药物（$5$ - $10$活性组分）表示。我们提出了一个约束制定CMF估计稀疏表型。我们通过对范德比尔特大学医学中心的EHR数据进行广泛的实证研究来证明我们的模型的有效性。
英文摘要：
The increased availability of electronic health records (EHRs) have spearheaded the initiative for precision medicine using data driven approaches. Essential to this effort is the ability to identify patients with certain medical conditions of interest from simple queries on EHRs, or EHR-based phenotypes. Existing rule--based phenotyping approaches are extremely labor intensive. Instead, dimensionality reduction and latent factor estimation techniques from machine learning can be adapted for phenotype extraction with no (or minimal) human supervision. We propose to identify an easily interpretable latent space shared across various sources of EHR data as potential candidates for phenotypes. By incorporating multiple EHR data sources (e.g., diagnosis, medications, and lab reports) available in heterogeneous datatypes in a generalized \textit{Collective Matrix Factorization (CMF)}, our methods can generate rich phenotypes. Further, easy interpretability in phenotyping application requires sparse representations of the candidate phenotypes, for example each phenotype derived from patients' medication and diagnosis data should preferably be represented by handful of diagnosis and medications, ($5$--$10$ active components). We propose a constrained formulation of CMF for estimating sparse phenotypes. We demonstrate the efficacy of our model through an extensive empirical study on EHR data from Vanderbilt University Medical Center.
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