基于LLM的使用EHR的少样本疾病预测：一种结合预测智能体推理与批判智能体指令的新方法
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
AI总结：
本研究提出一种结合预测智能体与批判智能体的LLM新方法，将EHR数据转为自然语言进行疾病预测，实现了优于传统方法的少样本性能。
摘要：
电子健康记录（EHRs）包含用于健康相关预测任务（如疾病预测）的有价值患者数据。传统方法依赖监督学习方法，需要大量标注数据集，这既昂贵又难以获取。在本研究中，我们研究应用大型语言模型（LLMs）将结构化患者就诊数据（例如诊断、化验、处方）转换为自然语言叙述的可行性。我们使用各种面向EHR预测的提示策略评估LLMs的零样本和少样本性能。此外，我们提出一种利用具有不同角色的LLM智能体的新方法：一个进行预测并生成推理过程的预测智能体，以及一个分析错误预测并为改进预测智能体推理提供指导的批判智能体。我们的结果表明，使用所提出的方法，与传统监督学习方法相比，LLMs在基于EHR的疾病预测中能实现良好的少样本性能，表明其在健康导向应用中的潜力。
英文摘要：
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.
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