用入院首日数据预测重症患者死亡风险，提升救治效率
Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review
- 整合机器学习与新型生物标志物，挖掘首日临床数据价值
- 相比传统评分系统，新方法显著提升早期死亡预测准确率
- 适合临床决策支持系统开发者及重症医学研究者参考
重症监护室（ICU）收治大量危重患者，其中许多人面临高死亡风险。在入院24小时内实现对住院死亡的早期精准预测，对于及时开展临床干预、优化资源配置及改善患者预后至关重要。传统评分系统虽具应用价值，但在预测准确性与适应性方面存在局限。本文旨在系统评估并对比基于入院首日可用数据的创新预测方法，重点关注机器学习技术、新型生物标志物应用以及多源数据融合等方面的进展。
原文摘要 · Abstract (English)
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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