# Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

arXiv: https://arxiv.org/abs/2112.05195 · HTML: https://mlsanity.com/paper/2112.05195/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Pre-training of Graph Augmented Transformers for Medication Recommendation](https://mlsanity.com/paper/1906.00346/) (2019)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer](https://mlsanity.com/paper/1906.04716/) (2019)
- [Deepr: A Convolutional Net for Medical Records](https://mlsanity.com/paper/1607.07519/) (2016)
- [ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context](https://mlsanity.com/paper/1911.12216/) (2019)
- [Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare](https://mlsanity.com/paper/2105.07542/) (2021)
- [TAPER: Time-Aware Patient EHR Representation](https://mlsanity.com/paper/1908.03971/) (2019)

## Built on by (most cited first, 20 of 20)

- [Multi-Label Clinical Time-Series Generation via Conditional GAN](https://mlsanity.com/paper/2204.04797/) (2022)
- [HealthFlow: A Self-Evolving AI Agent with Meta Planning for Autonomous Healthcare Research](https://mlsanity.com/paper/2508.02621/) (2025)
- [SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction](https://mlsanity.com/paper/2405.09039/) (2024)
- [Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction](https://mlsanity.com/paper/2501.17326/) (2025)
- [No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery](https://mlsanity.com/paper/2505.16288/) (2025)
- [Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning](https://mlsanity.com/paper/2310.09672/) (2023)
- [Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction](https://mlsanity.com/paper/2502.10689/) (2025)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Multi-modal Graph Learning over UMLS Knowledge Graphs](https://mlsanity.com/paper/2307.04461/) (2023)
- [Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction](https://mlsanity.com/paper/2404.14815/) (2024)
- [From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR](https://mlsanity.com/paper/2406.05682/) (2024)
- [MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records](https://mlsanity.com/paper/2411.11161/) (2024)
- [RuCCoD: Towards Automated ICD Coding in Russian](https://mlsanity.com/paper/2502.21263/) (2025)
- [CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction](https://mlsanity.com/paper/2511.11423/) (2025)
- [IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Models](https://mlsanity.com/paper/2408.13073/) (2024)
- [DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data](https://mlsanity.com/paper/2504.11264/) (2025)
- [THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction](https://mlsanity.com/paper/2506.17844/) (2025)
- [Structure-aware Hypergraph Transformer for Diagnosis Prediction in Electronic Health Records](https://mlsanity.com/paper/2508.20500/) (2025)
- [MATA-Former & SIICU: Semantic Aware Temporal Alignment for High-Fidelity ICU Risk Prediction](https://mlsanity.com/paper/2604.01727/) (2026)
- [Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation](https://mlsanity.com/paper/2607.18270/) (2026)

## Abstract (arXiv)

With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common feature of EHR data used for deep-learning-based predictions is historical diagnoses. Existing work mainly regards a diagnosis as an independent disease and does not consider clinical relations among diseases in a visit. Many machine learning approaches assume disease representations are static in different visits of a patient. However, in real practice, multiple diseases that are frequently diagnosed at the same time reflect hidden patterns that are conducive to prognosis. Moreover, the development of a disease is not static since some diseases can emerge or disappear and show various symptoms in different visits of a patient. To effectively utilize this combinational disease information and explore the dynamics of diseases, we propose a novel context-aware learning framework using transition functions on dynamic disease graphs. Specifically, we construct a global disease co-occurrence graph with multiple node properties for disease combinations. We design dynamic subgraphs for each patient's visit to leverage global and local contexts. We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes. Experimental results on two real-world EHR datasets show that the proposed model outperforms state of the art in predicting health events.