Semi-supervised learning of the electronic health record for phenotype stratification
Semi-supervised learning of the electronic health record for phenotype stratification
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DOI：
10.1016/j.jbi.2016.10.007
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发表时间：
2016-12-01
影响因子：
4.5
通讯作者：
Greene, Casey S.
中科院分区：
文献类型：
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作者：
Beaulieu-Jones, Brett K.;Greene, Casey S.
患者与医疗保健提供者的互动会导致电子健康记录（EHRs）的输入。电子病历是为临床和计费目的而构建的，但它包含关于个人的许多数据点。挖掘这些记录为提取电子表型提供了机会，这些电子表型可以与遗传数据配对，以识别人类常见疾病的基因。这项任务仍然具有挑战性：高质量的表型分析成本高昂，需要医生审查；记录中的许多字段是稀疏填充的；随着时间的推移，我们对疾病的定义也在不断改进。在这里，我们开发和评估了一种半监督学习方法，用于EHR表型提取，使用去噪自编码器进行表型分层。通过将去噪自动编码器与随机森林相结合，我们发现了多个模拟模型的分类改进，并改善了ALS临床试验数据的生存预测。这在只有少数患者具有高质量表型的情况下尤其明显，这是基于ehr的研究中常见的情况。去噪自动编码器执行降维，实现可视化和聚类，以发现新的疾病亚型。这种方法代表了一种有希望的方法来澄清疾病亚型和改善利用电子病历的基因型-表型关联研究。(C) 2016作者。Elsevier Inc.出版。
Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purposes but contain many data points about an individual. Mining these records provides opportunities to extract electronic phenotypes, which can be paired with genetic data to identify genes underlying common human diseases. This task remains challenging: high quality phenotyping is costly and requires physician review; many fields in the records are sparsely filled; and our definitions of diseases are continuing to improve over time. Here we develop and evaluate a semi supervised learning method for EHR phenotype extraction using denoising autoencoders for phenotype stratification. By combining denoising autoencoders with random forests we find classification improvements across multiple simulation models and improved survival prediction in ALS clinical trial data. This is particularly evident in cases where only a small number of patients have high quality phenotypes, a common scenario in EHR-based research. Denoising autoencoders perform dimensionality reduction enabling visualization and clustering for the discovery of new subtypes of disease. This method represents a promising approach to clarify disease subtypes and improve genotype-phenotype association studies that leverage EHRs. (C) 2016 The Author(s). Published by Elsevier Inc.