通过直接领域建模和LLM生成的启发式方法实现个性化药物规划
Personalized Medication Planning via Direct Domain Modeling and LLM-Generated Heuristics
AI总结：
本文通过直接领域建模和LLM生成的启发式方法，提升了个性化药物规划的规模和效率，支持更接近临床应用的实践。
摘要：
个性化药物规划涉及选择药物并确定给药方案，以实现每个个体患者特定的医疗目标。先前的工作成功证明，使用通用领域无关启发式的自动化规划器能够生成个性化治疗，当领域和问题通过通用领域描述语言（\pddlp）建模时。然而，这一过程在实践中受限于最多只能考虑七种药物。在临床术语中，这并不现实。在本文中，我们探索了使用自动生成的领域和问题特定启发式方法与通用搜索相结合，作为扩大药物规划规模以更接近与临床医生合作的方法。具体而言，我们通过编程指定领域（指定初始状态和后继生成过程），并使用LLM生成问题特定的启发式方法，该方法可以被固定搜索算法（GBFS）使用。结果表明，覆盖范围和规划时间有显著提高，将药物数量扩大到至少28种，并使药物规划更接近实际应用。
英文摘要：
Personalized medication planning involves selecting medications and determining a dosing schedule to achieve medical goals specific to each individual patient. Previous work successfully demonstrated that automated planners, using general domain-independent heuristics, are able to generate personalized treatments, when the domain and problems are modeled using a general domain description language (\pddlp). Unfortunately, this process was limited in practice to consider no more than seven medications. In clinical terms, this is a non-starter. In this paper, we explore the use of automatically-generated domain- and problem-specific heuristics to be used with general search, as a method of scaling up medication planning to levels allowing closer work with clinicians. Specifically, we specify the domain programmatically (specifying an initial state and a successor generation procedure), and use an LLM to generate a problem specific heuristic that can be used by a fixed search algorithm (GBFS). The results indicate dramatic improvements in coverage and planning time, scaling up the number of medications to at least 28, and bringing medication planning one step closer to practical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
