# Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission

arXiv: https://arxiv.org/abs/2204.06766 · HTML: https://mlsanity.com/paper/2204.06766/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Neural networks versus Logistic regression for 30 days all-cause readmission prediction](https://mlsanity.com/paper/1812.09549/) (2018)

## Built on by (most cited first, 14 of 14)

- [Zero Shot Health Trajectory Prediction Using Transformer](https://mlsanity.com/paper/2407.21124/) (2024)
- [HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction](https://mlsanity.com/paper/2602.05286/) (2026)
- [FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction](https://mlsanity.com/paper/2406.11928/) (2024)
- [Graph AI in Medicine](https://mlsanity.com/paper/2310.13767/) (2023)
- [Graph Neural Networks for Heart Failure Prediction on an EHR-Based Patient Similarity Graph](https://mlsanity.com/paper/2411.19742/) (2024)
- [Prediction of 30-day hospital readmission with clinical notes and EHR information](https://mlsanity.com/paper/2503.23050/) (2025)
- [Information Flow in Graph Neural Networks: A Clinical Triage Use Case](https://mlsanity.com/paper/2309.06081/) (2023)
- [Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking](https://mlsanity.com/paper/2505.01696/) (2025)
- [Atherosclerosis through Hierarchical Explainable Neural Network Analysis](https://mlsanity.com/paper/2507.07373/) (2025)
- [Structure-aware Hypergraph Transformer for Diagnosis Prediction in Electronic Health Records](https://mlsanity.com/paper/2508.20500/) (2025)
- [Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark](https://mlsanity.com/paper/2602.19502/) (2026)
- [EveryQuery: A Promptable Foundation Model for Clinical Prediction Tasks over Electronic Health Records](https://mlsanity.com/paper/2603.07900/) (2026)
- [Mr.Dec: Daily-Scale Longitudinal Multimodal Modeling for 30-Day Readmission Prediction](https://mlsanity.com/paper/2608.16929/) (2026)
- [Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models](https://mlsanity.com/paper/2609.12277/) (2026)

## Abstract (arXiv)

Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged. While recent deep learning-based studies have shown promising empirical results on readmission prediction, several limitations exist that may hinder widespread clinical utility, such as (a) only patients with certain conditions are considered, (b) existing approaches do not leverage data temporality, (c) individual admissions are assumed independent of each other, which is unrealistic, (d) prior studies are usually limited to single source of data and single center data. To address these limitations, we propose a multimodal, modality-agnostic spatiotemporal graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission that fuses multimodal in-patient longitudinal data. By training and evaluating our methods using longitudinal chest radiographs and electronic health records from two independent centers, we demonstrate that MM-STGNN achieves AUROC of 0.79 on both primary and external datasets. Furthermore, MM-STGNN significantly outperforms the current clinical reference standard, LACE+ score (AUROC=0.61), on the primary dataset. For subset populations of patients with heart and vascular disease, our model also outperforms baselines on predicting 30-day readmission (e.g., 3.7 point improvement in AUROC in patients with heart disease). Lastly, qualitative model interpretability analysis indicates that while patients' primary diagnoses were not explicitly used to train the model, node features crucial for model prediction directly reflect patients' primary diagnoses. Importantly, our MM-STGNN is agnostic to node feature modalities and could be utilized to integrate multimodal data for triaging patients in various downstream resource allocation tasks.