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机器学习用于预测喘患者中的喘恶化:系统性审查和元分析
Shiqiu Xiong1,2, Wei Chen3, Xinyu Jia3
1Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Children's Hospital of Capital Institute of Pediatrics, Beijing, 100020, China. xsq20180224@student.pumc.edu.cn.
机器学习 (ML) 模型在预测喘恶化方面表现有前途,增强方法获得了最高的性能 (AUROC 0.84). 需要进一步的研究来改善模型的概括性和临床适用性.
科学领域:
- 肺部医学 肺部医学
- 生物统计学 生物统计学
- 人工智能的人工智能
背景情况:
- 喘恶化严重影响患者的生活质量,并导致大量的医疗保健费用.
- 使用机器学习 (ML) 的预测模型越来越多地被开发用于预测喘恶化.
- 本系统性审查和元分析评估了ML模型对喘恶化的预测性能.
结论:
- 机器学习显示出在准确预测喘恶化方面有很大的潜力.
- 模型方法的异质性需要进一步研究.
- 未来的研究应该优先考虑增强模型通用性和实际临床应用.
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