mlsanityData to 7 Oct 2026

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

arXiv 2111.06152 ↗First posted 11 Nov 2021cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 36 references that are in the map. First posted on arXiv 11 Nov 2021.

Builds on

11 of 36 references.

References · 11

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 763
  4. 2017Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysiscited by 145
  5. 2020Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scalecited by 39
  6. 2018Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Featurescited by 36
  7. 2018Deep learning cardiac motion analysis for human survival predictioncited by 31
  8. 2018Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Recordcited by 30
  9. 2020Temporal Phenotyping using Deep Predictive Clustering of Disease Progressioncited by 26
  10. 2019Measuring Patient Similarities via a Deep Architecture with Medical Concept Embeddingcited by 11
  11. 2020Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patientscited by 2

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.

4 papers.

  1. 2023Temporal Supervised Contrastive Learning for Modeling Patient Risk Progressioncited by 10
  2. 2022Similarity-based prediction of Ejection Fraction in Heart Failure Patientscited by 0
  3. 2022Enabling scalable clinical interpretation of ML-based phenotypes using real world datacited by 0
  4. 2025ICU-TSB: A Benchmark for Temporal Patient Representation Learning for Unsupervised Stratification into Patient Cohortscited by 0