Electronic health records (EHRs) contain valuable patient data for
health-related prediction tasks, such as disease prediction. Traditional
approaches rely on supervised learning methods that require large labeled
datasets, which can be expensive and challenging to obtain. In this study, we
investigate the feasibility of applying Large Language Models (LLMs) to convert
structured patient visit data (e.g., diagnoses, labs, prescriptions) into
natural language narratives. We evaluate the zero-shot and few-shot performance
of LLMs using various EHR-prediction-oriented prompting strategies.
Furthermore, we propose a novel approach that utilizes LLM agents with
different roles: a predictor agent that makes predictions and generates
reasoning processes and a critic agent that analyzes incorrect predictions and
provides guidance for improving the reasoning of the predictor agent. Our
results demonstrate that with the proposed approach, LLMs can achieve decent
few-shot performance compared to traditional supervised learning methods in
EHR-based disease predictions, suggesting its potential for health-oriented
applications.

使用大型语言模型，本研究探索将结构化的患者就诊数据（例如诊断、实验室和处方）转化为自然语言叙述的可行性，并提出了一种利用不同角色的语言模型代理（预测者代理和评论者代理）的新方法。结果表明，相比传统的监督学习方法，在基于电子健康记录的疾病预测中，使用该方法的大型语言模型能够实现相当不错的少样本性能，暗示其在健康相关应用中的潜力。