# Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare

arXiv: https://arxiv.org/abs/2105.07542 · HTML: https://mlsanity.com/paper/2105.07542/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 16 May 2021.

## Builds on (most cited first, 15 of 15)

- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Is Attention Interpretable?](https://mlsanity.com/paper/1906.03731/) (2019)
- [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 to Deceive with Attention-Based Explanations](https://mlsanity.com/paper/1909.07913/) (2019)
- [Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer](https://mlsanity.com/paper/1906.04716/) (2019)
- [MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare](https://mlsanity.com/paper/1810.09593/) (2018)
- [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)
- [AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration](https://mlsanity.com/paper/1911.12205/) (2019)
- [SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation](https://mlsanity.com/paper/1710.05980/) (2017)

## Built on by (most cited first, 30 of 30)

- [GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs](https://mlsanity.com/paper/2305.12788/) (2023)
- [REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models](https://mlsanity.com/paper/2402.07016/) (2024)
- [RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records](https://mlsanity.com/paper/2403.00815/) (2024)
- [Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs](https://mlsanity.com/paper/2112.05195/) (2021)
- [EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.00036/) (2024)
- [Multi-Label Clinical Time-Series Generation via Conditional GAN](https://mlsanity.com/paper/2204.04797/) (2022)
- [Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction](https://mlsanity.com/paper/2106.04751/) (2021)
- [Predictive Modeling with Temporal Graphical Representation on Electronic Health Records](https://mlsanity.com/paper/2405.03943/) (2024)
- [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)
- [Next Visit Diagnosis Prediction via Medical Code-Centric Multimodal Contrastive EHR Modelling with Hierarchical Regularisation](https://mlsanity.com/paper/2401.11648/) (2024)
- [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)
- [SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction](https://mlsanity.com/paper/2501.00190/) (2024)
- [Multi-Ontology Integration with Dual-Axis Propagation for Medical Concept Representation](https://mlsanity.com/paper/2508.21320/) (2025)
- [Bridging Stepwise Lab-Informed Pretraining and Knowledge-Guided Learning for Diagnostic Reasoning](https://mlsanity.com/paper/2410.19955/) (2024)
- [Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction](https://mlsanity.com/paper/2502.10689/) (2025)
- [Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization](https://mlsanity.com/paper/2506.06977/) (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)
- [MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records](https://mlsanity.com/paper/2411.11161/) (2024)
- [CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction](https://mlsanity.com/paper/2511.11423/) (2025)
- [Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation](https://mlsanity.com/paper/2604.13331/) (2026)
- [MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering](https://mlsanity.com/paper/2204.10011/) (2022)
- [Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification](https://mlsanity.com/paper/2308.05385/) (2023)
- [IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Models](https://mlsanity.com/paper/2408.13073/) (2024)
- [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)
- [REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation](https://mlsanity.com/paper/2609.04415/) (2026)

## Abstract (arXiv)

Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep learning based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured text. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed model also utilizes unstructured text data by employing an attention regulation strategy and then integrates attentive text features into a sequential learning process. We conduct extensive experiments on two important healthcare problems to show the competitive prediction performance of the proposed method compared with various state-of-the-art models. We also confirm the effectiveness of learned representations and model interpretability by a set of ablation and case studies.