Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.
复制标题
DOI：
10.1038/srep26094
复制
发表时间：
2016-05-17
影响因子：
4.6
通讯作者：
Dudley JT
中科院分区：
文献类型：
--
作者：
Miotto R;Li L;Kidd BA;Dudley JT
电子健康记录（EHR）的二次利用有望推动临床研究，并更好地为临床决策提供信息。在总结和呈现患者数据方面存在的挑战阻碍了使用电子健康记录进行预测建模的广泛实践。在此，我们提出一种新的无监督深度特征学习方法，从电子健康记录数据中得出一种通用的患者表征，以促进临床预测建模。具体而言，我们使用了一个三层的去噪自动编码器堆栈，从西奈山数据仓库中约70万名患者的聚合电子健康记录中捕捉层级规律和相关性。其结果是一种我们称之为“深度患者”的表征。我们通过评估患者患各种疾病的概率，将这种表征作为对健康状态的广泛预测进行了评估。我们使用了76214名测试患者进行评估，这些患者涵盖了来自不同临床领域和时间窗口的78种疾病。我们的结果显著优于使用基于原始电子健康记录数据的表征和其他特征学习策略所取得的结果。对严重糖尿病、精神分裂症和各种癌症的预测表现处于领先水平。这些发现表明，应用于电子健康记录的深度学习能够得出可提供更优临床预测的患者表征，并可为增强临床决策系统提供一个机器学习框架。
Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.