机器学习模型用于癌症相关血栓形成风险预测:系统性审查和元分析
Keya Chen1, Ying Zhang2, Lufang Zhang1
1The First School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Journal of thrombosis and haemostasis : JTH
|November 16, 2024
概括
机器学习模型显示出与癌症相关的血栓形成风险的优异预测. 然而,大多数研究都有很高的偏差风险,尽管可以接受模型的适用性.
科学领域:
- 医疗信息学 医疗信息学
- 在瘤学瘤学.
- 血液学 血液学 血液学
背景情况:
- 越来越多的癌症相关血栓形成的预测模型.
- 缺乏对机器学习 (ML) 预测模型的全面评估.
- 在这个领域,需要对ML模型进行批判性评估.
研究的目的:
- 批判性地评估ML模型用于预测癌症相关的血栓形成.
- 量化这些ML预测模型的性能.
- 评估偏差风险和现有模型的适用性.
主要方法:
- 在多个数据库 (PubMed,Embase等) 进行系统的文献搜索. 在2023年12月之前.
- 使用预测模型偏差风险评估工具 (PMROC) 进行偏差和适用性评估.
- 采用推评估,开发和证据 (GRADE) 系统对证据质量的评级.
- 使用R (版本4.3.2) 进行了元分析.
主要成果:
- 在审查中包括了32项研究.
- 主要的文献表现出偏见的高风险.
- 预测模型的适用性通常是可以接受的.
- 对21项研究的元分析证实了ML模型对癌症相关血栓的高预测能力.
结论:
- ML模型显示出良好的适用性和与癌症相关的血栓形成的良好预测性能.
- 在大多数纳入的研究中,发现了明显高的偏差风险.
- 需要进一步的研究来解决偏差并提高模型可靠性.
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