mlsanityData to 14 Oct 2026

Interpreting deep embeddings for disease progression clustering

arXiv 2307.06060 ↗First posted 12 Jul 2023stat.ML cs.CL cs.LGcited by 1

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Abstract (arXiv)

We propose a novel approach for interpreting deep embeddings in the context of patient clustering. We evaluate our approach on a dataset of participants with type 2 diabetes from the UK Biobank, and demonstrate clinically meaningful insights into disease progression patterns.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 48 references that are in the map. First posted on arXiv 12 Jul 2023.

Builds on

17 of 48 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  4. 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
  5. 2020Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease predictioncited by 373
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  8. 2022GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Recordscited by 238
  9. 2020Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Datacited by 87
  10. 2020Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scalecited by 39
  11. 2020Temporal Phenotyping using Deep Predictive Clustering of Disease Progressioncited by 26
  12. 2021Visualizing Data using GTSNEcited by 24
5 more
  1. 2020Learning outside the Black-Box: The pursuit of interpretable modelscited by 18
  2. 2021An explainable Transformer-based deep learning model for the prediction of incident heart failurecited by 12
  3. 2018What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systemscited by 11
  4. 2019Learning Multimorbidity Patterns from Electronic Health Records Using Non-negative Matrix Factorisationcited by 8
  5. 2022sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansioncited by 3

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning modelcited by 0