用大模型生成用药规划启发式，让自动化方案支持最多28种药物。
Personalized Medication Planning via Direct Domain Modeling and LLM-Generated Heuristics
- 程序化定义用药域，用大模型生成针对性启发式。
- 可处理至少28种药物，规划覆盖率和速度显著提升。
- 适合临床辅助决策系统开发人员参考。
个性化用药规划旨在为每位患者选择药物并确定剂量方案以达成特定医疗目标。以往研究虽证明基于通用领域无关启发式的方法在使用通用领域描述语言（PDDL）建模时能生成个性化治疗方案，但实际应用受限于最多仅能考虑七种药物，临床意义有限。本文探索利用自动生成的领域与问题特定启发式，配合通用搜索算法，以扩展用药规划能力。具体而言，通过程序化方式定义领域（包括初始状态和后继生成过程），并借助大模型生成针对具体问题的启发式，供固定搜索算法（GBFS）使用。实验结果表明，该方法在覆盖率和规划时间上均有显著提升，将可处理药物数量扩展至至少28种，使用药规划更接近临床实用场景。
原文摘要 · Abstract (English)
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.
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