Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
This work addresses the challenge of scalable patient stratification from EHRs for personalized medicine, though it is incremental as it builds on existing deep learning techniques.
The authors tackled the problem of deriving disease subtypes from electronic health records (EHRs) by developing an unsupervised deep learning framework called ConvAE, which processes heterogeneous EHR data to create patient representations for scalable stratification. The result showed that ConvAE significantly outperformed baselines in clustering tasks, achieving average scores of 2.61 entropy and 0.31 purity, and identified clinically relevant subtypes for conditions like type 2 diabetes and Parkinson's disease.
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale. We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising of a total of 57,464 clinical concepts. We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to transform patient trajectories into low-dimensional latent vectors. We evaluated these representations as broadly enabling patient stratification by applying hierarchical clustering to different multi-disease and disease-specific patient cohorts. ConvAE significantly outperformed several baselines in a clustering task to identify patients with different complex conditions, with 2.61 entropy and 0.31 purity average scores. When applied to stratify patients within a certain condition, ConvAE led to various clinically relevant subtypes for different disorders, including type 2 diabetes, Parkinson's disease and Alzheimer's disease, largely related to comorbidities, disease progression, and symptom severity. With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights. This scalable framework can help better understand varying etiologies in heterogeneous sub-populations and unlock patterns for EHR-based research in the realm of personalized medicine.