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. A primary goal of precision medicine is to develop quantitative models for patients that can be used to predict health status, as well as to help prevent disease or disability. In this context, electronic health records (EHRs) offer great promise for accelerating clinical research and predictive analysis 1 . Recent studies have shown that secondary use of EHRs has enabled data-driven prediction of drug effects and interactions 2 , identification of type 2 diabetes subgroups 3 , discovery of comorbidity clusters in autism spectrum disorders 4 , and improvements in recruiting patients for clinical trials 5 . However, predictive models and tools based on modern machine learning techniques have not been widely and reliably used in clinical decision support systems or workflows [6] [7] [8] [9] . EHR data is challenging to represent and model due to its high dimensionality, noise, heterogeneity, sparseness, incompleteness, random errors, and systematic biases 7, 10, 11 . Moreover, the same clinical phenotype can be expressed using different codes and terminologies. For example, a patient diagnosed with "type 2 diabetes mellitus" can be identified by laboratory values of hemoglobin A1C greater than 7.0, presence of 250.00 ICD-9 code, "type 2 diabetes mellitus" mentioned in the free-text clinical notes, and so on. These challenges have made it difficult for machine learning methods to identify patterns that produce predictive clinical models for real-world applications 12 . The success of predictive algorithms largely depends on feature selection and data representation 12, 13 . A common approach with EHRs is to have a domain expert designate the patterns to look for (i.e., the learning task and the targets) and to specify clinical variables in an ad-hoc manner 7 . Although appropriate in some situations, supervised definition of the feature space scales poorly, does not generalize well, and misses opportunities to discover novel patterns and features. To address these shortcomings, data-driven approaches for feature selection in EHRs have been proposed [14] [15] [16] . A limitation of these methods is that patients are often represented as
REF designed an unsupervised deep feature learning method to derive a patient representation from EHR data, thus to facilitate clinic predictive modeling.
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
Scientific Reports
Scientific Reports
Computer Science Medicine