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使用机器学习方法预测多药耐药或耐利素结核病早期治疗结果,以提高患者治愈率:多种模型的开发和验证

Fuzhen Zhang^1,2,3, Zilong Yang⁴, Xiaonan Geng²

¹Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, China.

Journal of medical Internet research

|September 22, 2025

概括

机器学习模型使用早期培养转换数据准确预测多药耐药结核病 (MDR/RR-TB) 治疗结果. 人工神经网络模型在提高患者预后和控制策略方面显示出卓越的性能.

科学领域:

医疗信息学医疗信息学
传染性疾病传染性疾病
医疗保健中的机器学习

背景情况:

早期预测多耐药或耐利素结核病 (MDR/RR-TB) 治疗结果对于患者的预后和疾病传播控制至关重要.
目前缺乏对MDR/RR-TB治疗结果的验证预测模型,这构成了重大挑战.

研究的目的:

将后勤回归与机器学习 (ML) 模型进行比较,以预测2个月和6个月的MDR/RR-TB治疗结果.
改进预测模型应用,完善控制策略,提高MDR/RR-TB治愈率.

主要方法:

对744名患有MDR/RR-TB的患者 (内部队列) 和137名患有MDR/RR-TB的患者 (外部队列) 的追溯分析.
开发和验证后勤回归和7ML模型,以预测2个月和6个月的培养转换.
使用曲线下的面积,精度,灵敏度和特异性的性能评估.

主要成果:

人工神经网络模型在预测2个月和6个月的培养转换方面表现优异 (AUC 0.82-0.90).
该模型实现了高精度,灵敏度和特异性,超过了传统的物流回归.
在内部队列中,2个月和6个月的文化转换率分别为81.9%和87.1%.

结论:

机器学习模型,特别是人工神经网络,可以根据早期培养转换准确预测MDR/RR-TB治疗结果.
这些ML模型为在MDR/RR-TB治疗的早期阶段评估治疗疗效提供了快速有效的工具.
与MDR/RR-TB结果预测的后勤回归相比,ML模型显示出更高的稳定性和通用性.

关键词:

人工智能的人工智能是人工智能.文化转换,文化转换.机器学习是机器学习.耐多药或耐瑞芬的结核病.预测预测预测预测治疗疗效治疗疗效治疗疗效

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