学习用于医疗指导的个性化提示
Learning Personalized Prompts for Healthcare Guidance
AI总结：
该研究针对现有LLM医疗指导缺乏个性化的问题，提出PPL框架，利用患者自身与同类病例信息构建初始提示并经RL优化，在妇产科数据上获专家97%的偏好，可与专有LLM无缝集成。
摘要：
大型语言模型（LLMs）的快速发展已改变包括医疗保健在内的众多行业。实际应用中，医院和患者越来越需要基于LLM的系统，这类系统能解读个人健康记录并提供医疗指导。然而，现有方法主要依赖通用医学知识，往往未考虑个体差异，限制了其提供个性化指导的能力。为解决该问题，我们提出个性化提示学习（PPL）框架，该框架学习个性化提示以引导LLM生成个性化医疗建议。PPL利用患者自身信息及临床相似病例衍生的同类信号构建初始个性化提示，随后使用强化学习（RL）优化这些提示，使生成的响应更贴合为每位患者撰写的医生建议。PPL采用硬提示，无需修改底层模型即可与专有LLM无缝集成。我们在真实妇产科数据上评估PPL，结果显示该方法能生成更具个性化的医疗指导，在专家评估的100次对比中胜出97次，证明其在更广泛医疗应用中的潜力。我们的代码可在此httpsURL公开获取。
英文摘要：
The rapid development of large language models (LLMs) has transformed many industries, including healthcare. In practice, hospitals and patients increasingly seek LLM-based systems capable of interpreting personal health records and providing healthcare guidance. However, existing approaches mainly rely on general medical knowledge and often fail to account for individual variability, limiting their ability to provide personalized guidance. To address this, we propose personalized prompt learning (PPL), a framework that learns individualized prompts to guide LLMs in generating personalized healthcare recommendations. PPL constructs initial personalized prompts by leveraging both self-informed patient information and peer-informed signals derived from clinically similar cases. These prompts are then refined using reinforcement learning (RL) to better align the generated responses with physician recommendations written for each patient. PPL operates with hard prompts, enabling seamless integration with proprietary LLMs without modifying the underlying models. We evaluate PPL on real-world obstetrics and gynecology data. The results show that our approach produces more personalized healthcare guidance and wins 97 out of 100 comparisons in expert evaluation, demonstrating its potential for broader healthcare applications. Our code is publicly available at https://github.com/CGCL-codes/PPL.
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。
