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arXiv · 2505.12344

Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review

Baozhu Huang·Cheng Chen·Xuanhe Hou·Junmin Huang·Zihan Wei·Hongying Luo·Lu Chen·Yongzhi Xu·Hejiao Luo·Changqi Qin·Ziqian Bi·Junhao Song·Tianyang Wang·ChiaXin Liang·Zizhong Yu·Han Wang·Xiaotian Sun·Junfeng Hao·Chunjie Tian
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Abstract

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.

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Baozhu Huang, Cheng Chen, Xuanhe Hou, Junmin Huang, Zihan Wei, Hongying Luo, Lu Chen, Yongzhi Xu, Hejiao Luo, Changqi Qin, Ziqian Bi, Junhao Song, Tianyang Wang, ChiaXin Liang, Zizhong Yu, Han Wang, Xiaotian Sun, Junfeng Hao, Chunjie Tian. 2025-05-18. Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review. https://arxiv.org/abs/2505.12344

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