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一种可解释的机器学习方法,用于预测临床重要胃肠道出血在重症患者

Shohei Ono¹, Shigehiko Uchino², Shinshu Katayama³

¹Department of Anesthesiology and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan; Department of Emergency and Intensive Care Medicine, Tokyo Metropolitan Tama Medical Center, Tokyo, Japan.

Anaesthesia, critical care & pain medicine

|July 11, 2025

概括

机器学习准确地预测了ICU患者临床重要胃肠道出血 (CIGIB). 确定了关键预测因素,可以改善早期干预和患者的治疗结果.

科学领域:

临界护理医学临界护理医学
医疗保健中的机器学习
预测分析是一种预测分析.

背景情况:

临床上重要的胃肠道出血 (CIGIB) 是重症患者的重大并发症,与急症监护室停留和死亡率的增加有关.
目前用于识别高风险CIGIB患者的现有方法是不够的.
之前的研究还没有利用机器学习来预测ICU中的CIGIB或确定其关键预测因子.

研究的目的:

开发和验证机器学习模型,用于预测重症监护室 (ICU) 患者的CIGIB.
使用机器学习解释性技术识别CIGIB的关键临床预测因素.

主要方法:

一项回顾性单中心研究包括2017年至2024年期间入院的7357名ICU患者.
机器学习模型 (XGBoost,随机森林,L1-逻辑回归) 被训练在ICU入院前24小时的数据上.
模型性能使用AUROC,精度,回忆和F1分数进行评估,用于预测器评估的SHAP值.

主要成果:

XGBoost模型实现了最高的预测性能,AUROC为0.84.
CIGIB的主要预测因素包括APACHE III分数,血红素,APTT,肌素和呼吸速率.
侵入性机械通风和压力预防并不是前20大预测因素之一.

结论:

这项研究是首次将机器学习应用于ICU患者的CIGIB预测,证明了高准确性和可解释性.
开发的模型显示了指导早期干预和预防策略的潜力.
建议进行进一步的多中心研究和干预试验,以验证这些发现.

关键词:

临床上重要的胃肠道出血.极端的梯度增强了极端的梯度.机器学习是机器学习.沙普利添加剂的解释压力的预防压力的预防

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