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基于机器学习的预测工具和临床癌症患者在医院死亡率的诺莫:使用回顾性队列的开发和外部验证

Kaier Gu¹, Saisai Lu²

¹Department of Internal Medicine, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, Zhejiang, China.

BMC medical informatics and decision making

|July 4, 2025

概括

新的模型预测了严重癌症患者的死亡率. 后勤回归 (LR) 和极度梯度提升 (XGB) 显示出强大的准确性,有助于为更好的患者结果做出临床决策.

科学领域:

在瘤学瘤学.
关键护理医学关键护理医学
生物统计学生物统计学
医疗保健中的机器学习

背景情况:

在癌症患者中,重症监护室 (ICU) 住院的高发病率和死亡率.
现有的评分系统缺乏预测这一群体结果的特异性.
需要准确的早期预测病情危急的癌症患者的住院死亡率.

研究的目的:

建立和验证一个预测模型,用于早期预测重症癌症患者的住院死亡率.
为了比较各种机器学习模型在死亡率预测方面的有效性.
开发一个帮助临床决策的工具.

主要方法:

对eICU和MIMIC-IV数据库的回顾性分析.
使用最小绝对收缩和选择操作员 (LASSO) 进行特征选择.
开发并比较了六种机器学习模型,包括物流回归 (LR) 和极端梯度增强 (XGB).
使用曲线下的面积 (AUC) 进行外部验证和性能评估.
纳米图和沙普利增量解释 (SHAP) 用于模型的解释性.

主要成果:

为模型开发确定了12个预测因素.
在外部验证中,LR和XGB模型表现出最佳的预测性能,AUC分别为0.751和0.737.
命名图和SHAP值为模型变量提供了可解释性.
创建了一个用户友好的基于Web的计算器工具.

结论:

LR和XGB模型有效地预测了重症癌症患者的住院死亡率.
这些模型表现出强大的预测能力,在外部队列中得到验证.
开发的模型可以帮助临床医生及时做出决策和干预,以改善患者护理.

关键词:

癌症癌症癌症癌症住院死亡率住院死亡率在重症监护病房的重症监护病房.机器学习是机器学习.恶性瘤是一种恶性瘤.预测预后预测预测

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