mlsanityData to 7 Oct 2026

StageNet: Stage-Aware Neural Networks for Health Risk Prediction

arXiv 2001.10054 ↗First posted 24 Jan 2020cs.LG cs.AI stat.MLcited by 47

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Abstract (arXiv)

Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction. StageNet is enabled by (1) a stage-aware long short-term memory (LSTM) module that extracts health stage variations unsupervisedly; (2) a stage-adaptive convolutional module that incorporates stage-related progression patterns into risk prediction. We evaluate StageNet on two real-world datasets and show that StageNet outperforms state-of-the-art models in risk prediction task and patient subtyping task. Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets. StageNet also achieves over 58% higher Calinski-Harabasz score (a cluster quality metric) for a patient subtyping task.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2020.

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Citing papers per year: 2021: 6; 2022: 5; 2023: 6; 2024: 12; 2025: 9; 2026: 9.

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  2. 2021Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologiescited by 34
  3. 2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
  4. 2024Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Datacited by 22
  5. 2024RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Recordscited by 21
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  9. 2024Recent Advances in Predictive Modeling with Electronic Health Recordscited by 11
  10. 2022A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Carecited by 10
  11. 2021PyHealth: A Python Library for Health Predictive Modelscited by 9
  12. 2021SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Recordscited by 9
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  3. 2022Artificial Intelligence for In Silico Clinical Trials: A Reviewcited by 6
  4. 2023Predict and Interpret Health Risk using EHR through Typical Patientscited by 5
  5. 2024Multi-task Heterogeneous Graph Learning on Electronic Health Recordscited by 5
  6. 2025Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseasescited by 5
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  8. 2021MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learningcited by 4
  9. 2022Deep Stable Representation Learning on Electronic Health Recordscited by 3
  10. 2023PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applicationscited by 3
  11. 2024CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analyticscited by 3
  12. 2022CATNet: Cross-event Attention-based Time-aware Network for Medical Event Predictioncited by 2
  13. 2025Self-Explaining Hypergraph Neural Networks for Diagnosis Predictioncited by 2
  14. 2025Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Lazinesscited by 2
  15. 2025ProtoEHR: Hierarchical Prototype Learning for EHR-based Healthcare Predictionscited by 2
  16. 2021Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learningcited by 1
  17. 2023Causal Discovery with Stage Variables for Health Time Seriescited by 1
  18. 2023Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasetscited by 1
  19. 2025KAT-GNN: A Knowledge-Augmented Temporal Graph Neural Network for Risk Prediction in Electronic Health Recordscited by 1
  20. 2025Grounded by Experience: Generative Healthcare Prediction Augmented with Hierarchical Agentic Retrievalcited by 1
  21. 2026PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learningcited by 1
  22. 2026DT-BEHRT: Disease Trajectory-aware Transformer for Interpretable Patient Representation Learningcited by 1
  23. 2021Temporal Clustering with External Memory Network for Disease Progression Modelingcited by 0
  24. 2022MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clusteringcited by 0
  25. 2024MPRE: Multi-perspective Patient Representation Extractor for Disease Predictioncited by 0
  26. 2024IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Modelscited by 0
  27. 2025Structure-aware Hypergraph Transformer for Diagnosis Prediction in Electronic Health Recordscited by 0
  28. 2025Chain-of-Influence: Tracing Interdependencies Across Time and Features in Clinical Predictive Modelingscited by 0
  29. 2026A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Studycited by 0
  30. 2026Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Predictioncited by 0
  31. 2026RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Modelscited by 0
  32. 2026HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Carecited by 0
  33. 2026Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generationcited by 0
  34. 2026Patients-like-me: A Variational LM--GNN Framework for Explainable Clinical Predictioncited by 0
  35. 2026MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Recordscited by 0