ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction

ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction

Accurate prediction of intensive care unit (ICU) length of stay (LOS) is crucial for optimizing resource allocation and improving patient outcomes. We propose ContrastLOS, a novel graph-based deep learning model that integrates graph transformer networks with contrastive pre-training to enhance ICU...

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Bibliographic Details
Main Authors: Guangrui Fan, Aixiang Liu, Chao Zhang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Contrastive pre-training
graph-based learning
healthcare AI
intensive care unit (ICU)
length of stay (LOS) prediction
Online Access:https://ieeexplore.ieee.org/document/10883945/
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Summary:Accurate prediction of intensive care unit (ICU) length of stay (LOS) is crucial for optimizing resource allocation and improving patient outcomes. We propose ContrastLOS, a novel graph-based deep learning model that integrates graph transformer networks with contrastive pre-training to enhance ICU LOS prediction. By constructing dynamic patient similarity graphs, ContrastLOS captures intricate inter-patient relationships and temporal dependencies. Through contrastive learning on unlabeled data, the model develops robust patient embeddings suitable for both classification and regression tasks. Experiments on three comprehensive ICU datasets—MIMIC-III, MIMIC-IV v3.0, and eICU—show that ContrastLOS consistently outperforms state-of-the-art baselines. For classification (predicting whether LOS exceeds 3 or 7 days), it achieves AUROC scores of up to 82.1%, improving performance by up to 5.2% compared to competitive methods. In regression tasks, ContrastLOS attains the lowest RMSE (2.41 days) and MAE (1.48 days) on the multi-center eICU dataset. Notably, it maintains an AUROC of 76.8% with only 10% labeled data, highlighting its effectiveness in low-resource settings. These findings suggest that ContrastLOS can serve as a robust clinical decision support tool for critical care management.
ISSN:2169-3536

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