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开发和验证一种机器学习模型,用于预测结直肠癌手术后静脉血栓栓塞并发症

Zongsheng Sun¹, Di Hao², Mingyu Yang¹

¹Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China.

Visual computing for industry, biomedicine, and art

|September 11, 2025

概括

一个机器学习模型有效地预测了结直肠癌 (CRC) 手术患者的静脉血栓栓塞 (VTE) 风险. 这种工具有助于对高风险个体进行及时的预防性抗凝药,改善患者的治疗结果.

科学领域:

医疗信息学医疗信息学
在瘤学瘤学.
血管外科血管外科

背景情况:

手术后静脉血栓塞栓症 (VTE) 是结直肠癌 (CRC) 手术的重大并发症,导致患者预后不佳.
目前的方法缺乏有效的工具,用于早期查和预测VTE在这个患者群体.
开发预测模型对于及时干预和改善结果至关重要.

研究的目的:

开发和验证基于机器学习 (ML) 的模型,用于预测CRC手术后静脉瘤风险.
用独立的外部数据集来评估ML模型的性能.
为早期识别高风险患者提供针对性预防性抗凝药的工具.

主要方法:

总共有3227名CRC手术患者被纳入,数据分为培训 (1596名患者) 和外部验证 (716名患者) 组.
六个ML算法使用堆叠组合技术来开发预测模型.
该模型在培训套件上进行了十次交叉验证,并在独立的外部验证套件上进行了测试.

主要成果:

堆叠ML模型在外部验证集中表现出卓越的性能.
关键性能指标包括接收器操作特征曲线下的面积为0.840,准确度为0.810,特异性为0.819,灵敏度为0.741,回忆率为0.959.
该模型成功识别了风险较高的VTE患者,包括深静脉血栓和肺栓塞.

结论:

开发的堆叠ML模型显示了在手术CRC患者中及时临床检测高风险VTE病例的显著前景.
这种预测工具可以促进在高风险人群中优先实施预防性抗凝药.
该模型可能有助于减轻低风险患者群体中不必要的药理干预,优化治疗策略.

关键词:

结肠直肠癌是一种癌症.复杂性复杂性复杂性机器学习是机器学习.预测模型的预测模型.手术手术手术术术静脉血栓塞栓症是一种静脉血栓塞栓症.

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