基于机器学习的肺栓塞预测,以减少胃肠道癌症患者不必要的计算机断层扫描:一项回顾性多中心研究
Joo Seong Kim1,2, Doyun Kwon3, Kyungdo Kim4,5
1Department of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Scientific reports
|October 25, 2024
概括
机器学习模型可以预测胃肠道癌症患者的肺栓塞 (PE),减少不必要的CTPA扫描. 这种人工智能工具可以提高高风险个体的诊断效率和患者护理.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 医疗信息学 医疗信息学
背景情况:
- 胃肠道癌症患者面临更高的肺栓塞 (PE) 风险.
- 目前在这个人群中对PE的诊断策略可能会导致不必要的计算机断层肺血管造影 (CTPA) 程序.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测胃肠道癌症患者的PE.
- 评估该模型能够减少CTPA扫描的需求的能力.
主要方法:
- 使用人口统计和临床数据进行回顾性多中心研究 (2010-2020年).
- 一个随机的森林ML模型被训练并使用威尔斯分数和D-二次数水平进行验证.
- 模型性能根据接收器操作曲线 (AUROC) 下的面积进行评估.
主要成果:
- ML模型实现了AUROC的0.736 (医院A) 和0.669 (医院B).
- 与传统方法相比,ML模型显著降低了CTPA建议 (P < 0.001对于A医院,P = 0.003对于B医院).
结论:
- 一个基于ML的预测模型显示了在胃肠道癌症患者中减少不必要的CTPA程序的潜力.
- 这种方法可以提高诊断效率,减轻患者的负担.
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