  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.
