mlsanityData to 7 Oct 2026

RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism

arXiv 1608.05745 ↗First posted 19 Aug 2016cs.LG cs.AI cs.NEcited by 344

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Abstract (arXiv)

Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are important. We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention. RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.

Key facts. Cited by 344 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Aug 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 71; 2022: 42; 2023: 51; 2024: 54; 2025: 68; 2026: 57.

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  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2021AdaRNN: Adaptive Learning and Forecasting of Time Seriescited by 120
  3. 2021Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Seriescited by 96
  4. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  5. 2023MOTOR: A Time-To-Event Foundation Model For Structured Medical Recordscited by 75
  6. 2021Explaining Time Series Predictions with Dynamic Maskscited by 72
  7. 2021Time Series Prediction using Deep Learning Methods in Healthcarecited by 71
  8. 2022Concept Activation Regions: A Generalized Framework For Concept-Based Explanationscited by 57
  9. 2023GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphscited by 57
  10. 2021Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health recordscited by 53
  11. 2021SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinationscited by 51
  12. 2021Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progressioncited by 48
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  2. 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35
  3. 2021Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologiescited by 34
  4. 2023CPLLM: Clinical Prediction with Large Language Modelscited by 34
  5. 2024TimeX++: Learning Time-Series Explanations with Information Bottleneckcited by 32
  6. 2024On the Challenges and Opportunities in Generative AIcited by 31
  7. 2021Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Seriescited by 30
  8. 2021Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcarecited by 30
  9. 2023Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosiscited by 30
  10. 2023Causal Interpretation of Self-Attention in Pre-Trained Transformerscited by 30
  11. 2021Heterogeneous Similarity Graph Neural Network on Electronic Health Recordscited by 29
  12. 2021Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classificationcited by 29
  13. 2022Conditional Generation Net for Medication Recommendationcited by 28
  14. 2021Temporal Dependencies in Feature Importance for Time Series Predictionscited by 27
  15. 2022SEAT: Stable and Explainable Attentioncited by 25
  16. 2024Large Language Model Distilling Medication Recommendation Modelcited by 25
  17. 2021ARM-Net: Adaptive Relation Modeling Network for Structured Datacited by 24
  18. 2021Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embeddingcited by 24
  19. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
  20. 2022WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Valuescited by 24
  21. 2023A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?cited by 24
  22. 2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
  23. 2023Learning Perturbations to Explain Time Series Predictionscited by 23
  24. 2024ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaborationcited by 23
  25. 2022PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learningcited by 22
  26. 2024Explaining Time Series via Contrastive and Locally Sparse Perturbationscited by 22
  27. 2024RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Recordscited by 21
  28. 2021Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphscited by 20
  29. 2024CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRTcited by 20
  30. 2021Pretrained Language Models are Symbolic Mathematics Solvers too!cited by 17
  31. 2021Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Predictioncited by 15
  32. 2021Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution Viewcited by 15
  33. 2023Interpretation of Time-Series Deep Models: A Surveycited by 15
  34. 2021AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Unitscited by 14
  35. 2021Alignment Attention by Matching Key and Query Distributionscited by 14
  36. 2021Natural Language Processing for Smart Healthcarecited by 14
  37. 2022POETREE: Interpretable Policy Learning with Adaptive Decision Treescited by 14
  38. 2018Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networkscited by 13

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