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开发基于机器学习的预测模型,用于在重症监护病房中与呼吸衰竭相关的急性损伤

Huayang Qin¹, Qimeng Wu², Jiaxiong Tan³

¹Department of Urology, Anyang People's Hospital, 1120 Yuefei Street, Anyang, 455000, Henan Province, China.

Clinical and experimental medicine

|October 25, 2025

概括

机器学习准确地预测了急性损伤 (AKI) 在重症监护室 (ICU) 患有呼吸衰竭的患者. 这种早期预测模型使用常规数据来改善患者的治疗结果并减少医疗保健负担.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
关键护理医学关键护理医学
数据科学数据科学数据科学

背景情况:

急性损伤 (AKI) 是重症监护室 (ICU) 呼吸衰竭患者的重大并发症,导致高死亡率和医疗费用增加.
现有的风险评分 (SOFA,APACHE II) 在预测该特定患者群体内的AKI方面存在局限性.
早期和准确的AKI预测对于及时干预和改善患者管理至关重要.

研究的目的:

开发和验证一种机器学习模型,用于在急症监护室呼吸衰竭患者中早期预测AKI.
确定该人群中AKI发展的关键预测因素.
为了使风险分层为主动的AKI预防和管理.

主要方法:

从MIMIC-IV数据库分析了10,780名患有呼吸衰竭的成年ICU患者.
训练10个监督学习算法,使用来自ICU入院前48小时的数据.
使用递归特征消除进行特征选择,确定每个模型的前15个预测因素.
使用AUC,精度,灵敏度和特异性等指标验证模型性能.

主要成果:

极端梯度增强 (XGBoost) 模型表现出卓越的性能,其AUC为0.9023.
通过SHAP分析确定的关键预测因素包括肌_max,住院时间,BUN_max,先前存在的脏疾病和尿液输出.
该模型在预测AKI时实现了高精度 (0.8247) 和灵敏度 (0.8077) .

结论:

使用常规可用的早期ICU数据的机器学习模型可以准确地预测呼吸衰竭患者的AKI.
开发的模型提供可解释的风险分层,支持主动预防策略.
建议进行进一步的外部验证和EHR集成用于临床实施,以个性化AKI管理.

关键词:

急性损伤是什么?急性损伤是什么?在ICU中,医生会对患者进行治疗.机器学习机器学习呼吸系统衰竭呼吸系统衰竭在XGBoost上使用.

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