Predicting Patient Outcomes with Graph Representation Learning

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Learning Representations of Missing Data for Predicting Patient OutcomesPreprint

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Benchmarking machine learning models on eICU critical care datasetPreprint

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Graph representation learning in biomedicine and healthcare

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Graph neural networks in histopathology: Emerging trends and future directions

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Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients' diagnoses

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An Integrated LSTM-HeteroRGNN Model for Interpretable Opioid Overdose Risk Prediction

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Graph Artificial Intelligence in Medicine

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Leveraging graph neural networks for supporting automatic triage of patients

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EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes

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LLM- Based Personalized Recommendations in HealthPreprint

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Multimodal fusion network for ICU patient outcome prediction

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Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs

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Fair patient model: Mitigating bias in the patient representation learned from the electronic health records

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Graph Neural Networks for Tabular Data Learning

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Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

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Length of stay prediction for hospital management using domain adaptation

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Securing Data From Side-Channel Attacks: A Graph Neural Network-Based Approach for Smartphone-Based Side Channel Attack Detection

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Graph neural networks for the enhanced prediction of new atrial fibrillation episodes after stroke

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Machine learning for prediction of childhood mental health problems in social care

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Leveraging patient similarities via graph neural networks to predict phenotypes from temporal data

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ICU-TGNN: A Hybrid Multitask Transformer and Graph Neural Network Model for Predicting Clinical Outcomes of Patients in the ICU

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Client Recruitment for Federated Learning in ICU Length of Stay Prediction

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Constructing and learning heterogeneous patient graph representations from structured electronic medical records

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Multi-organ spatiotemporal information aware model for sepsis mortality prediction

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Advancing Tabular Data Classification with Graph Neural Networks: A Random Forest Proximity Method

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Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data

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Systematic Literature Review on Deep Learning Techniques in Electronic Health Records

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A Multi-Graph Fusion Framework for Patient Representation Learning

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LAI-Pool: Persistent Homology-Informed Graph Neural Networks for Predicting Mortality in ICU Patients

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Understanding deep learning models for Length of Stay prediction on critically ill patients through latent space visualization

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Understanding Deep Learning for LoS prediction via Latent space visualizationPreprint

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Understanding Deep Learning for LoS prediction via Latent space visualizationPreprint

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Multi-Criteria-based Graph Neural Networks for a Medical Emergency Response System

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Bridging Graph and State-Space Modeling for Intensive Care Unit Length of Stay PredictionPreprint

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ICU-Length of Stay Prediction on Electronic Health Records using Graph Neural Networks and Homogeneous Similarity Graphs

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LAI-Pool: Persistent Homology-Informed Graph Neural Networks for Predicting Mortality in ICU Patients

Fanning, C., Verkouteren, C., Sawadogo, D., & Aktas, M. E. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-16649-4_12 · Cited by 1 · see citing works

A hybrid clinical temporal graph neural network model for time series data to predict health deterioration in ICU patients

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Interpretable Adaptive Graph Fusion Network for Mortality and Complication Prediction in ICUs

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