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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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10883945/ |
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| author | Guangrui Fan Aixiang Liu Chao Zhang |
| author_facet | Guangrui Fan Aixiang Liu Chao Zhang |
| author_sort | Guangrui Fan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d8115f6690574ea0a326228799b69ff3 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-d8115f6690574ea0a326228799b69ff32025-08-20T03:11:15ZengIEEEIEEE Access2169-35362025-01-0113341323414810.1109/ACCESS.2025.354089610883945ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay PredictionGuangrui Fan0https://orcid.org/0009-0001-8570-1636Aixiang Liu1https://orcid.org/0000-0001-5621-2502Chao Zhang2Department of Computer Science and Technology, Taiyuan University of Science and Technology, Wanbailin, Taiyuan, Shanxi, ChinaDepartment of Health Information Management, Fenyang College, Shanxi Medical University, Fenyang, Shanxi, ChinaDepartment of Health Information Management, Fenyang College, Shanxi Medical University, Fenyang, Shanxi, ChinaAccurate 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.https://ieeexplore.ieee.org/document/10883945/Contrastive pre-traininggraph-based learninghealthcare AIintensive care unit (ICU)length of stay (LOS) prediction |
| spellingShingle | Guangrui Fan Aixiang Liu Chao Zhang ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction IEEE Access Contrastive pre-training graph-based learning healthcare AI intensive care unit (ICU) length of stay (LOS) prediction |
| title | ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction |
| title_full | ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction |
| title_fullStr | ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction |
| title_full_unstemmed | ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction |
| title_short | ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction |
| title_sort | contrastlos a graph based deep learning model with contrastive pre training for improved icu length of stay prediction |
| topic | Contrastive pre-training graph-based learning healthcare AI intensive care unit (ICU) length of stay (LOS) prediction |
| url | https://ieeexplore.ieee.org/document/10883945/ |
| work_keys_str_mv | AT guangruifan contrastlosagraphbaseddeeplearningmodelwithcontrastivepretrainingforimprovediculengthofstayprediction AT aixiangliu contrastlosagraphbaseddeeplearningmodelwithcontrastivepretrainingforimprovediculengthofstayprediction AT chaozhang contrastlosagraphbaseddeeplearningmodelwithcontrastivepretrainingforimprovediculengthofstayprediction |