Document Type

Conference Paper

Publication Date

2025

DOI

10.1145/3765612.3767211

Publication Title

BCB '25: Proceedings of the 16th International Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages

43 (6 pp)

Conference Name

16th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, October 12-15, 2025, Philadelphia, PA

Abstract

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into the GNN layers, followed by Multi-Layer Perceptron (MLP) layers, which further transform the aggregated node representation from message passing to enhance the performance of LoS classification. We also systematically evaluate multiple machine learning, GNN, and graph transformer models with various feature configurations. Results obtained by applying our models to the MIMIC-III database demonstrate (i) a GraphSAGE encoder followed by an artificial neural network classifier (SAGE_ANN) and (ii) a two-layer GraphSAGE model (SAGE_2L) consistently yield superior performance in global hospital LoS prediction. This study highlights the impact of multi-modal fusion and graph learning in advancing predictive modeling for complex clinical systems.

Rights

© 2025 Copyright held by the owner/authors.

This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Al Musawi, A. F., Rana, P., Raha, S., Braunstein, J., Sleeman, W. C., Kapoor, R., & Ghosh, P. (2025). ICU-length of stay prediction on electronic health records using graph neural networks and homogeneous similarity graphs. In BCB '25: Proceedings of the 16th International Conference on Bioinformatics, Computational Biology, and Health Informatics (Article 43). Association for Computing Machinery, Inc. https://doi.org/10.1145/3765612.3767211

ORCID

0000-0001-9199-2479 (Pratip)

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