开发用于预测和诊断肺炎的机器学习方法的评估:系统性审查
Azam Kheirdoust1, Fatemeh Barzanouni2, Alireza Rasoulian2
1Department of Medical Informatics, School of Medicine Mashhad University of Medical Sciences Mashhad Iran.
Health science reports
|December 2, 2025
概括
机器学习模型显著提高了肺炎的诊断和预测. 本次审查强调了像DenseNet和ResNet这样的高性能模型,同时指出了数据偏差和临床使用的概括性方面的挑战.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 肺炎的流行需要先进的诊断和预测工具.
- 机器学习 (ML) 模型为管理复杂的健康数据集提供了能力.
- 本综述侧重于用于肺炎诊断,预测和治疗的ML模型.
研究的目的:
- 系统地评估肺炎ML模型的性能和质量.
- 遵守统计报告准则 (阿塞尔等人,2018年).
主要方法:
- 系统审查于2024年1月15日进行.
- 搜索的数据库:PubMed,Scopus,科学网,谷歌学者.
- 遵循PRISMA检查清单;包括开发/验证肺炎ML模型的文章.
- 提取的性能指标:准确度,灵敏度,特异性,AUC与置信区间.
主要成果:
- 从11545篇选文章中,包括了42项研究和125个ML模型.
- 对于诊断,DenseNet实现了94%的准确性 (95% CI:92%-96%).
- 对于预测,随机森林 (AUC 0.96) 和XGBoost (AUC 0.97) 是最有效的.
- 神经网络达到98.94%的准确性,ResNet达到99.63%的准确性.
- 确定了数据集偏差和有限的概括性.
结论:
- ML算法显著改善了肺炎的诊断和预测.
- 通过先进的ML工具优化临床决策.
- 需要进行标准化报告和强有力的验证,因为已经确定了偏见和通用性挑战.
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