可解释疾病诊断的大型语言模型与答案集编程证明概念
A Proof-of-Concept for Explainable Disease Diagnosis Using Large Language Models and Answer Set Programming
AI总结：
本研究提出McCoy框架，结合大型语言模型与答案集编程，实现可解释的疾病诊断，初步结果显示其在小规模诊断任务中表现优异。
AI中文摘要：
准确的疾病预测对于及时干预、有效治疗和减少医疗并发症至关重要。尽管符号AI已应用于医疗领域，但其应用仍受制于构建高质量知识库所需的努力。本工作引入了McCoy框架，该框架结合大型语言模型（LLMs）与答案集编程（ASP）以克服这一障碍。McCoy协调一个LLM将医学文献翻译成ASP代码，将其与患者数据结合，并通过ASP求解器处理以得出最终诊断。这种整合产生了一个稳健、可解释的预测框架，结合了两种范式的优势。初步结果表明，McCoy在小规模疾病诊断任务上表现出色。
英文摘要：
Accurate disease prediction is vital for timely intervention, effective treatment, and reducing medical complications. While symbolic AI has been applied in healthcare, its adoption remains limited due to the effort required for constructing high-quality knowledge bases. This work introduces McCoy, a framework that combines Large Language Models (LLMs) with Answer Set Programming (ASP) to overcome this barrier. McCoy orchestrates an LLM to translate medical literature into ASP code, combines it with patient data, and processes it using an ASP solver to arrive at the final diagnosis. This integration yields a robust, interpretable prediction framework that leverages the strengths of both paradigms. Preliminary results show McCoy has strong performance on small-scale disease diagnosis tasks.
