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机器学习模型的开发和验证,用于预测结直肠癌患者的静脉血栓栓塞:中国的一组研究

Zuhai Hu¹, Xiaosheng Li², Yuliang Yuan¹

¹Chongqing Cancer Multiomics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China.

International journal of medical informatics

|December 28, 2024

概括

这项研究开发了一种机器学习模型,用于预测结直肠癌患者的静脉血栓栓塞 (VTE) 风险. 随机森林 (RF) 模型显示出高准确性,为这一患者群体提供了更好的风险评估.

科学领域:

在瘤学瘤学.
数据科学数据科学数据科学
医疗信息学医疗信息学

背景情况:

传统的静脉血栓塞栓症 (VTE) 风险评估工具对于现代医疗保健来说是不够的.
需要专门的和创新的VTE风险评估方法,特别是对于癌症患者.

研究的目的:

开发用于VTE风险评估的可靠和高效的机器学习模型.
为了利用结直肠癌患者的基本数据和临床特征进行VTE预测.

主要方法:

利用六个机器学习算法来预测直肠癌患者的静脉瘤.
采用LASSO回归用于变量选择和5倍交叉验证用于超参数调整.
使用AUC,ACC和F1分数评估模型性能.

主要成果:

随机森林 (RF) 模型在内部和外部验证数据集中表现出卓越的性能.
在内部验证中,RF模型获得了高精度 (例如,AD-AUC 0.895,AD-ACC 0.871).
射频模型在外部验证数据 (ACC 0.728,F1 0.292) 上表现强,即使使用了替代失衡处理技术.

结论:

在结直肠癌患者中成功建立并验证了基于机器学习的VTE风险评估模型.
开发的RF模型为改善VTE风险分层在这个人群中提供了一个有效的工具.

关键词:

在 Bootstrap 中使用 Bootstrap.结肠直肠癌是一种癌症.机器学习是机器学习.这是一个VTE,VTE是VTE.

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