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在使用输血作为代用标记物的体外膜氧化患者中,基于机器学习的出血风险的预测

Tadashi Kamio^1,2, Masaru Ikegami³, Megumi Mizuno³

¹Department of Anesthesiology and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.

|April 25, 2025

概括

机器学习模型准确地预测了体外膜氧化 (ECMO) 患者的出血风险,使用红细胞 (RBC) 输血作为标记. 该工具有助于早期识别高风险个体,以改善输血管理.

科学领域:

临界护理医学临界护理医学
医疗信息学医疗信息学
血液学血液学血液学

背景情况:

体外膜氧化 (ECMO) 的使用正在增加,增加出血并发症的风险.
目前对ECMO患者的输血策略缺乏优化.
需要准确的预测工具来管理ECMO患者的出血.

研究的目的:

开发和验证用于预测ECMO患者出血并发症的机器学习 (ML) 算法.
为了利用红细胞 (RBC) 输血事件作为出血的替代标记.
确定影响出血风险预测的关键因素.

主要方法:

利用来自71家医院 (2018-2022) 的数据来开发ML模型.
采用LightGBM和随机森林模型,以精确度,回忆,F1得分和准确度评估性能.
进行了SHapley添加式解释 (SHAP) 分析,以确定预测因素的重要性.

主要成果:

开发了ML模型 (LightGBM,随机森林),ROC AUC高于0.7,用于预测出血并发症.
通过使用红细胞输血作为代孕标记物,获得了70.5%的准确性和0.784的召回.
SHAP分析确定循环血液体积,血红蛋白和体重是关键的预测因素.

结论:

轻GBM和随机森林模型在预测ECMO相关出血并发症方面表现出有效性.
开发的工具可以促进早期识别高风险患者.
这种预测能力可以增强ECMO患者的输血管理策略.

关键词:

在ECMO中,可以使用ECMO.出血并发症出血并发症机器学习模型机器学习模型输血是输血的方法之一.

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