强化学习优化结核病查策略在资源有限的环境
Prakash Nandkumar Kalavadekar1, Kiran Ramesh Khandarkar2, Dhanraj R Dhotre3
1Department of Computer Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, 423603, India.
The Indian journal of tuberculosis
|December 16, 2025
概括
本研究引入了强化学习 (RL) 以优化结核病 (TB) 查,旨在在资源有限的环境中更快,更便宜地检测病例. 临床临床框架提高了查效率和适应性,以获得更好的全球健康结果.
科学领域:
- 计算流行病学计算流行病学
- 机器学习在公共卫生中的应用.
- 全球传染病控制全球传染病控制
背景情况:
- 结核病 (TB) 仍然是一个重大的全球卫生挑战,特别是在资源有限的地区.
- 现有的结核病查方法 (X射线,症状评估,分子诊断) 在成本,可扩展性和有效性方面存在局限性.
- 迫切需要创新的策略来提高结核病例检测速度和效率.
研究的目的:
- 开发和评估基于强化学习 (RL) 的框架,以优化结核病查方案.
- 解决各种医疗保健环境中成本效益和快速病例识别的挑战.
- 创建适应性查政策,可以适应现实世界资源限制.
主要方法:
- 将结核病查作为RL范式中的一个顺序性决策问题.
- 定义状态空间 (患者病史,症状,风险,资源) 和行动空间 (测试,转诊,随访).
- 使用在真实世界和模拟结核病数据集上训练的RL算法 (Q-learning,DQN,Policy Gradient).
主要成果:
- 与基线方法相比,RL优化的协议显示了更好的查覆盖率和成本效益.
- 该框架显示了适应不同资源可用性和医疗保健环境的适应性.
- 评估的绩效指标包括病例检测率,假阴性率和每例检测成本.
结论:
- 强化学习提供了一种强大的方法来提高结核病查的效率和适应性.
- 拟议的RL框架可以导致全球更有效和资源意识的结核病例发现.
- 这种方法有望改善传染病管理中的公共卫生结果.
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