Objective: 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 a novel unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale. Materials and methods: 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. Results: ConvAE significantly outperformed several common 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. Conclusions: Patient representations derived from modeling EHRs with ConvAE can help develop personalized medicine therapeutic strategies and better understand varying etiologies in heterogeneous sub-populations.

利用基于深度学习的无监督框架从电子健康记录中提取患者表现，为下一代个体化医学提供指导。通过将病人轨迹转换为低维潜在向量来对1,608,741例患者的电子病历进行了综合分析，发现ConvAE可以生成导致临床有意义的见解的患者表示形式。

电子健康记录的深度表征学习以解锁大规模患者分层