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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.12344 |
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| _version_ | 1866908553645654016 |
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| author | Huang, Baozhu Chen, Cheng Hou, Xuanhe Huang, Junmin Wei, Zihan Luo, Hongying Chen, Lu Xu, Yongzhi Luo, Hejiao Qin, Changqi Bi, Ziqian Song, Junhao Wang, Tianyang Liang, ChiaXin Yu, Zizhong Wang, Han Sun, Xiaotian Hao, Junfeng Tian, Chunjie |
| author_facet | Huang, Baozhu Chen, Cheng Hou, Xuanhe Huang, Junmin Wei, Zihan Luo, Hongying Chen, Lu Xu, Yongzhi Luo, Hejiao Qin, Changqi Bi, Ziqian Song, Junhao Wang, Tianyang Liang, ChiaXin Yu, Zizhong Wang, Han Sun, Xiaotian Hao, Junfeng Tian, Chunjie |
| contents | The intensive care unit (ICU) manages critically ill patients, many of whom face a high risk of mortality. Early and accurate prediction of in-hospital mortality within the first 24 hours of ICU admission is crucial for timely clinical interventions, resource optimization, and improved patient outcomes. Traditional scoring systems, while useful, often have limitations in predictive accuracy and adaptability. Objective: This review aims to systematically evaluate and benchmark innovative methodologies that leverage data available within the first day of ICU admission for predicting in-hospital mortality. We focus on advancements in machine learning, novel biomarker applications, and the integration of diverse data types. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review Huang, Baozhu Chen, Cheng Hou, Xuanhe Huang, Junmin Wei, Zihan Luo, Hongying Chen, Lu Xu, Yongzhi Luo, Hejiao Qin, Changqi Bi, Ziqian Song, Junhao Wang, Tianyang Liang, ChiaXin Yu, Zizhong Wang, Han Sun, Xiaotian Hao, Junfeng Tian, Chunjie Machine Learning Computers and Society The intensive care unit (ICU) manages critically ill patients, many of whom face a high risk of mortality. Early and accurate prediction of in-hospital mortality within the first 24 hours of ICU admission is crucial for timely clinical interventions, resource optimization, and improved patient outcomes. Traditional scoring systems, while useful, often have limitations in predictive accuracy and adaptability. Objective: This review aims to systematically evaluate and benchmark innovative methodologies that leverage data available within the first day of ICU admission for predicting in-hospital mortality. We focus on advancements in machine learning, novel biomarker applications, and the integration of diverse data types. |
| title | Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2505.12344 |