人工智能的临床利用,用于预测肺结核病的治疗疗效
Fuzhen Zhang1, Fan Zhang2, Liang Li1
1Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, PR China; Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China.
Journal of infection and public health
|March 1, 2024
概括
人工智能 (AI) 可以通过预测持续时间,不良反应和耐药性来加强对肺结核 (PTB) 治疗的监测. 这项技术可以改善患者的预后,并为抗结核治疗提供未来的研究方向.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 结核病研究 结核病研究
背景情况:
- 监测肺结核 (PTB) 治疗疗效的传统方法在灵敏度上有局限性.
- 越来越需要先进的工具来改善抗结核病 (ATTB) 治疗的监测和管理.
- 人工智能 (AI) 为增强PTB治疗监测提供了一个有希望的途径.
研究的目的:
- 系统地审查人工智能在监测抗结核治疗 (ATTB) 疗效方面的应用.
- 确定有效预测PTB治疗持续时间,不良反应,结果和耐药性的AI算法.
- 提供关于人工智能的潜力,以改善患者的预后,并指导PTB治疗监测的未来研究.
主要方法:
- 在六个数据库中进行了系统的文献搜索.
- 该搜索涵盖了2013年至2023年间发表的研究,重点是AI应用在预测PTB治疗疗效方面.
- 分析了各种人工智能算法,包括支持向量机,卷积神经网络,随机森林,人工神经网络以及分类和回归树.
主要成果:
- 支持向量机器和卷积神经网络在预测治疗持续时间方面表现出卓越.
- 随机森林,人工神经网络和分类和回归树在预测不良反应和治疗结果方面表现有希望.
- 神经网络和随机森林被发现在预测耐药性方面是有效的.
结论:
- 人工智能技术具有显著的潜力,可以加强ATTB治疗的监测和管理.
- 人工智能的进步可以带来改进的监测策略和更好的患者预后.
- 本次审查强调了在PTB治疗监测领域继续进行人工智能研究的需要.
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