# Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction

arXiv: https://arxiv.org/abs/2308.12575 · HTML: https://mlsanity.com/paper/2308.12575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 64 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer](https://mlsanity.com/paper/1906.04716/) (2019)
- [TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2010.03526/) (2020)
- [AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration](https://mlsanity.com/paper/1911.12205/) (2019)
- [Benchmarking machine learning models on multi-centre eICU critical care dataset](https://mlsanity.com/paper/1910.00964/) (2019)
- [Heterogeneous Similarity Graph Neural Network on Electronic Health Records](https://mlsanity.com/paper/2101.06800/) (2021)
- [GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks](https://mlsanity.com/paper/2202.00211/) (2022)
- [Incorporating Symbolic Domain Knowledge into Graph Neural Networks](https://mlsanity.com/paper/2010.13900/) (2020)
- [Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding](https://mlsanity.com/paper/1902.03376/) (2019)
- [Compound Density Networks for Risk Prediction using Electronic Health Records](https://mlsanity.com/paper/2208.01320/) (2022)
- [Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRs](https://mlsanity.com/paper/2308.09896/) (2023)

## Built on by (most cited first, 1 of 1)

- [Similarity-Based Self-Construct Graph Model for Predicting Patient Criticalness Using Graph Neural Networks and EHR Data](https://mlsanity.com/paper/2508.00615/) (2025)

## Abstract (arXiv)

The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making. Existing methods focus on measuring the similarity between patients using deep neural networks to capture the hidden feature structures. However, the higher-order relationships are ignored, such as patient characteristics (e.g., diagnosis codes) and their causal effects on downstream clinical predictions. In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction. Evaluation using a publicly available eICU Collaborative Research Database indicates that our method achieves superior performance over the state-of-the-art models on mortality risk prediction. Moreover, the results of several case studies demonstrated the effectiveness and robustness of the model decisions.