# Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale

arXiv: https://arxiv.org/abs/2003.06516 · HTML: https://mlsanity.com/paper/2003.06516/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2020.

## Built on by (most cited first, 30 of 39)

- [GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning](https://mlsanity.com/paper/2207.09858/) (2022)
- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [Neighborhood Contrastive Learning Applied to Online Patient Monitoring](https://mlsanity.com/paper/2106.05142/) (2021)
- [Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding](https://mlsanity.com/paper/2108.03625/) (2021)
- [Estimating Redundancy in Clinical Text](https://mlsanity.com/paper/2105.11832/) (2021)
- [Unsupervised pre-training of graph transformers on patient population graphs](https://mlsanity.com/paper/2207.10603/) (2022)
- [SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction](https://mlsanity.com/paper/2405.09039/) (2024)
- [Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives](https://mlsanity.com/paper/2111.04898/) (2021)
- [Get your Foes Fooled: Proximal Gradient Split Learning for Defense against Model Inversion Attacks on IoMT data](https://mlsanity.com/paper/2201.04569/) (2022)
- [GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring](https://mlsanity.com/paper/2605.30865/) (2026)
- [Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics](https://mlsanity.com/paper/2308.11027/) (2023)
- [Representation learning to advance multi-institutional studies with electronic health record data from US and France](https://mlsanity.com/paper/2502.08547/) (2025)
- [Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis](https://mlsanity.com/paper/2106.12658/) (2021)
- [Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes](https://mlsanity.com/paper/2111.06152/) (2021)
- [MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records](https://mlsanity.com/paper/2407.00840/) (2024)
- [Knowledge Graph Augmented Large Language Models for Disease Prediction](https://mlsanity.com/paper/2512.01210/) (2025)
- [Representational Ethical Model Calibration](https://mlsanity.com/paper/2207.12043/) (2022)
- [Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification](https://mlsanity.com/paper/2401.07796/) (2024)
- [Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence](https://mlsanity.com/paper/2402.05802/) (2024)
- [HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks](https://mlsanity.com/paper/2403.18128/) (2024)
- [Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records](https://mlsanity.com/paper/2306.03179/) (2023)
- [Deep Normed Embeddings for Patient Representation](https://mlsanity.com/paper/2204.05477/) (2022)
- [Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records](https://mlsanity.com/paper/2303.08290/) (2023)
- [Interpreting deep embeddings for disease progression clustering](https://mlsanity.com/paper/2307.06060/) (2023)
- [Comparing representations of long clinical texts for the task of patient note-identification](https://mlsanity.com/paper/2503.24006/) (2025)
- [Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data](https://mlsanity.com/paper/2505.20731/) (2025)
- [Capture Timing-Attention of Events in Clinical Time Series](https://mlsanity.com/paper/2602.10385/) (2026)
- [HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering](https://mlsanity.com/paper/2604.21027/) (2026)
- [Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients](https://mlsanity.com/paper/2101.04013/) (2021)
- [Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding](https://mlsanity.com/paper/2111.09098/) (2021)

## Abstract (arXiv)

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.