用大模型迭代生成可计算表型，提升难治性高血压诊疗效率
Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models
- 通过生成-执行-调试-指导循环，让大模型逐步优化可计算表型代码
- 在6种不同复杂度表型上达到接近顶尖机器学习方法的准确率
- 仅需少量训练样本即可实现，适合临床决策支持系统快速部署
大语言模型在医疗问答和编程方面表现突出，但其生成可解释、可计算表型（CPs）的潜力尚未充分挖掘。本文研究大模型是否能为六种不同复杂度的高血压临床表型生成准确且简洁的可计算表型，以支持规模化临床决策。除评估零样本性能外，提出并验证了一种‘合成-执行-调试-指导’策略，利用数据驱动反馈迭代优化表型代码。结果表明，结合迭代学习的大模型能生成可解释且合理准确的程序，其性能接近当前最优机器学习方法，同时所需训练样本显著减少。
原文摘要 · Abstract (English)
Large language models (LLMs) have demonstrated remarkable capabilities for medical question answering and programming, but their potential for generating interpretable computable phenotypes (CPs) is under-explored. In this work, we investigate whether LLMs can generate accurate and concise CPs for six clinical phenotypes of varying complexity, which could be leveraged to enable scalable clinical decision support to improve care for patients with hypertension. In addition to evaluating zero-short performance, we propose and test a synthesize, execute, debug, instruct strategy that uses LLMs to generate and iteratively refine CPs using data-driven feedback. Our results show that LLMs, coupled with iterative learning, can generate interpretable and reasonably accurate programs that approach the performance of state-of-the-art ML methods while requiring significantly fewer training examples.
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