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通过机器学习预测儿童喘恶化:通过元分析进行系统性审查
Martina Votto1,2, Annalisa De Silvestri3, Lorenzo Postiglione1
1Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
机器学习模型在预测儿科喘恶化方面表现有前途. 住院预测模型的准确性很好,但需要进一步验证才能在临床上使用.
科学领域:
- 儿科呼吸系统医学 儿科呼吸系统医学
- 医疗保健中的人工智能
- 生物统计学和流行病学
背景情况:
- 儿童喘恶化 represent一个重要的医疗负担.
- 传统的喘风险评估工具是有限的.
- 人工智能 (AI) 提供了改进预测模型的潜力.
研究的目的:
- 系统地评估和量化机器学习 (ML) 算法的性能.
- 评估ML模型在预测儿童喘恶化的住院和急诊室 (ED) 访问方面的准确性.
主要方法:
- 按照PRISMA指南进行系统审查和元分析.
- 使用PROBAST工具评估的偏差风险和适用性.
- 该研究协议已登记在系统性审查的国际前性登记册中.
主要成果:
- 包括7篇文章与17个基于ML的预测模型.
- 预测ED入院的AUC是0.67 (中等准确度).
- 医院住院预测的AUC是0.79 (良好的区分能力).
结论:
- 这是对儿童喘恶化人工智能算法的最全面的评估.
- 用于预测住院治疗的ML模型显示出很好的准确性.
- 在临床实施之前,外部验证至关重要.
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