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酒精性肝硬化患者28天全因死亡率:基于MIMIC-IV的机器学习预测模型

Chuang Lei¹, Zhixiang Ding¹, Qinghai Wang¹

¹Department of Infectious Diseases, Changde Hospital, Xiangya School of Medicine, Central South University (The First People's Hospital of Changde City), Changde, China.

Clinical and experimental medicine

|June 11, 2025

概括

机器学习模型可以预测酒精性肝硬化 (AC) 患者的28天死亡率. 这种方法比传统得分提供了更好的风险分层,有助于临床决策.

科学领域:

* 内部医学内部医学
* 医学信息学医学信息学
* 医疗保健中的机器学习

背景情况:

* 酒精性肝硬化 (AC) 具有显著的死亡风险.
*准确预测短期死亡率对于患者管理至关重要.
*现有的评分系统可能无法完全捕捉到AC死亡率的复杂性.

研究的目的:

*开发和验证一种机器学习 (ML) 模型,用于预测AC患者28天全因死亡率.
* 将ML模型的性能与传统评分系统进行比较.
* 确定AC中死亡率的关键预测因素.

主要方法:

*利用了MIMIC-IV数据库中的2134名AC患者的数据.
* 采用各种ML算法 (决策树,随机森林,XGBoost,物流回归,SVM) 来进行模型开发.
*分别对70%和30%的数据进行训练和验证模型;使用AUC,校准曲线和DCA评估性能;使用SHAP用于变量重要性.

主要成果:

*确定了关键的死亡预测因素,包括年龄,SOFA评分,ASPIII评分,OASIS评分,LODS评分,温度,化物,乳酸盐,总 bilirubin (Tbil),INR,APTT,中风,恶性疾病和先天性凝血缺陷 (CCD).
*开发的ML模型在训练和验证组中都表现出强大的预测性能.
* ML模型的性能超过了传统的MELD得分.

结论:

*ML预测模型有效地识别了28天死亡率高风险的AC患者.
* 该模型可以帮助临床医生早期风险分层,并为临床决策提供信息.
* 建议进行进一步的外部验证,以确认研究结果的概括性.

关键词:

酒精性肝硬化症是什么报告报告报告机器学习是机器学习.模仿者-IV 在线播放

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