mlsanityData to 14 Oct 2026

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

arXiv 2107.09951 ↗First posted 21 Jul 2021cs.LGcited by 34

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Abstract (arXiv)

Objective: Temporal electronic health records (EHRs) can be a wealth of information for secondary uses, such as clinical events prediction or chronic disease management. However, challenges exist for temporal data representation. We therefore sought to identify these challenges and evaluate novel methodologies for addressing them through a systematic examination of deep learning solutions. Methods: We searched five databases (PubMed, EMBASE, the Institute of Electrical and Electronics Engineers [IEEE] Xplore Digital Library, the Association for Computing Machinery [ACM] digital library, and Web of Science) complemented with hand-searching in several prestigious computer science conference proceedings. We sought articles that reported deep learning methodologies on temporal data representation in structured EHR data from January 1, 2010, to August 30, 2020. We summarized and analyzed the selected articles from three perspectives: nature of time series, methodology, and model implementation. Results: We included 98 articles related to temporal data representation using deep learning. Four major challenges were identified, including data irregularity, data heterogeneity, data sparsity, and model opacity. We then studied how deep learning techniques were applied to address these challenges. Finally, we discuss some open challenges arising from deep learning. Conclusion: Temporal EHR data present several major challenges for clinical prediction modeling and data utilization. To some extent, current deep learning solutions can address these challenges. Future studies can consider designing comprehensive and integrated solutions. Moreover, researchers should incorporate additional clinical domain knowledge into study designs and enhance the interpretability of the model to facilitate its implementation in clinical practice.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 51 of its 167 references that are in the map. First posted on arXiv 21 Jul 2021.

Builds on

51 of 167 references.

References · 51

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38 more
  1. 2017Multitask learning and benchmarking with clinical time series datacited by 290
  2. 2018Scalable and accurate deep learning for electronic health recordscited by 280
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 8; 2025: 11; 2026: 9.

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  1. 2021Benchmarking emergency department triage prediction models with machine learning and large public electronic health recordscited by 31
  2. 2023A scoping review on multimodal deep learning in biomedical images and textscited by 13
  3. 2024CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysiscited by 6
  4. 2022An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning modelscited by 4
  5. 2025Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysiscited by 4
  6. 2025TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Predictioncited by 4
  7. 2025Cross-Representation Benchmarking in Time-Series Electronic Health Records for Clinical Outcome Predictioncited by 4
  8. 2023Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentationcited by 3
  9. 2025Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Datacited by 3
  10. 2025Organ-Agents: Virtual Human Physiology Simulator via LLMscited by 3
  11. 2026The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Recordscited by 3
  12. 2024Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomescited by 2
22 more
  1. 2025Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classificationcited by 2
  2. 2022Shapley variable importance cloud for machine learning modelscited by 1
  3. 2024Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classificationcited by 1
  4. 2024Explainable Artificial Intelligence Techniques for Irregular Temporal Classification of Multidrug Resistance Acquisition in Intensive Care Unit Patientscited by 1
  5. 2024Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Datacited by 1
  6. 2025PA-RNet: Perturbation-Aware Residual Network for Robust Multimodal Time Series Forecastingcited by 1
  7. 2026User-Adaptive Meta-Learning for Cold-Start Medication Recommendation with Uncertainty Filteringcited by 1
  8. 2026Capture Timing-Attention of Events in Clinical Time Seriescited by 1
  9. 2026DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal datacited by 1
  10. 2023tSPM+; a high-performance algorithm for mining transitive sequential patterns from clinical datacited by 0
  11. 2024Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learningcited by 0
  12. 2024Explainable AI for Classifying UTI Risk Groups Using a Real-World Linked EHR and Pathology Lab Datasetcited by 0
  13. 2024PT: A Plain Transformer is Good Hospital Readmission Predictorcited by 0
  14. 2025Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representationscited by 0
  15. 2025MedM2T: A MultiModal Framework for Time-Aware Modeling with Electronic Health Record and Electrocardiogram Datacited by 0
  16. 2025BiPETE: A Bi-Positional Embedding Transformer Encoder for Risk Assessment of Alcohol and Substance Use Disorder with Electronic Health Recordscited by 0
  17. 2025MAPI-GNN: Multi-Activation Plane Interaction Graph Neural Network for Multimodal Medical Diagnosiscited by 0
  18. 2026Temporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant carecited by 0
  19. 2026Learning temporal embeddings from electronic health records of chronic kidney disease patientscited by 0
  20. 2026NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotypingcited by 0
  21. 2026AdaPCLA: Adaptive Prior-Calibrated Logit Adjustment for Long-Tailed Longitudinal EHR Generationcited by 0
  22. 2026CareGraph: An Auditable Hybrid AI Framework for Evidence-Grounded Personalized Longitudinal Health Intelligencecited by 0