# Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes

arXiv: https://arxiv.org/abs/2111.06152 · HTML: https://mlsanity.com/paper/2111.06152/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale](https://mlsanity.com/paper/2003.06516/) (2020)
- [Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features](https://mlsanity.com/paper/1802.01059/) (2018)
- [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018)
- [Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record](https://mlsanity.com/paper/1810.04793/) (2018)
- [Temporal Phenotyping using Deep Predictive Clustering of Disease Progression](https://mlsanity.com/paper/2006.08600/) (2020)
- [Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding](https://mlsanity.com/paper/1902.03376/) (2019)
- [Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients](https://mlsanity.com/paper/2012.13233/) (2020)

## Built on by (most cited first, 4 of 4)

- [Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression](https://mlsanity.com/paper/2312.05933/) (2023)
- [Similarity-based prediction of Ejection Fraction in Heart Failure Patients](https://mlsanity.com/paper/2203.07124/) (2022)
- [Enabling scalable clinical interpretation of ML-based phenotypes using real world data](https://mlsanity.com/paper/2208.01607/) (2022)
- [ICU-TSB: A Benchmark for Temporal Patient Representation Learning for Unsupervised Stratification into Patient Cohorts](https://mlsanity.com/paper/2506.06192/) (2025)

## Abstract (arXiv)

The increase in availability of longitudinal electronic health record (EHR) data is leading to improved understanding of diseases and discovery of novel phenotypes. The majority of clustering algorithms focus only on patient trajectories, yet patients with similar trajectories may have different outcomes. Finding subgroups of patients with different trajectories and outcomes can guide future drug development and improve recruitment to clinical trials. We develop a recurrent neural network autoencoder to cluster EHR data using reconstruction, outcome, and clustering losses which can be weighted to find different types of patient clusters. We show our model is able to discover known clusters from both data biases and outcome differences, outperforming baseline models. We demonstrate the model performance on 29,229 diabetes patients, showing it finds clusters of patients with both different trajectories and different outcomes which can be utilized to aid clinical decision making.