公共卫生决策分析的多因素强化学习框架.
Dinesh Sharma1, Ankit Shah2, Chaitra Gopalappa3
1University of South Florida, Department of Industrial and Management Systems Engineering, 4202 E Fowler Ave, Tampa, 33620, FL, USA.
Healthcare analytics (New York, N.Y.)
|February 2, 2026
概括
一个新的多代理强化学习框架优化了为结束艾滋病毒流行病倡议的资源配置. 这种方法解释了跨司法管辖区的相互作用,在减少新的艾滋病毒感染方面表现优于传统方法.
科学领域:
- 公共卫生 公共卫生
- 人工智能的人工智能
- 流行病学 流行病学
背景情况:
- 人类免疫缺陷病毒 (HIV) 在美国是一个重大的公共卫生挑战,其特点是疾病负担和获得护理的地理差异很大.
- "结束艾滋病毒流行病" (EHE) 倡议旨在通过改进诊断,治疗和预防,专注于高流行地区,到2030年将新感染减少90%.
- 目前针对艾滋病毒干预的决策分析模型有限,要么专注于单个地点,要么集中国家数据,因此错过了关键的跨司法管辖权动态.
研究的目的:
- 开发一个智能决策支持系统,以优化EHE倡议中的资源分配和干预策略.
- 提出一个新的多代理强化学习 (MARL) 框架,以促进特定司法管辖区的决策,同时考虑不同领域之间的流行病学相互作用.
- 创建一个系统,以动态,数据驱动的洞察力为基础,帮助决策者在战略性地为艾滋病毒预防和治疗分配资源.
主要方法:
- 为智能资源优化而设计的多代理强化学习 (MARL) 框架的开发.
- 在MARL框架内实施司法管辖区特定的决策能力,以捕捉跨司法管辖区的流行病学相互作用.
- 使用来自加利福尼亚州和佛罗里达州司法管辖区的数据对MARL框架的实验性评估.
主要成果:
- 与传统的单剂强化学习方法相比,MARL驱动的政策表现优越.
- 在固定预算限制下,拟议的框架有效地减少了新的艾滋病毒感染.
- 该研究证实了将司法依赖纳入大规模公共卫生计划的决策框架的重要性.
结论:
- 多代理强化学习为优化公共卫生倡议 (如EHE) 的资源配置提供了一种强有力的方法.
- 考虑到跨司法管辖区的流行病学相互作用对于有效的大规模流行病管理至关重要.
- 这项研究推进了政府资源规划和公共卫生管理的专家系统,为流行病控制提供了可扩展的框架.
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