评估大语言模型理解表格型电子健康记录的能力：患者数据提取与检索综合研究
Evaluating LLM Abilities to Understand Tabular Electronic Health Records: A Comprehensive Study of Patient Data Extraction and Retrieval
AI总结：
本研究首次探究大语言模型理解表格型电子健康记录的能力，通过MIMICSQL数据集实验验证了特征选择、序列化方法及上下文学习对任务表现的提升作用，并提出相关设计准则以支持健康搜索。
摘要：
电子健康记录（EHR）表格存在诸多独特挑战，其中包括医疗特征间存在隐藏的上下文依赖关系，且数据具有高维度与高稀疏性。本研究首次探究大语言模型（LLM）理解EHR以完成患者数据提取与检索的能力。我们使用MIMICSQL数据集开展大量实验，基于任务表现探究提示结构、指令、上下文与示例对Llama2和Meditron这两个骨干LLM的影响。通过定量与定性分析，我们的发现表明，与朴素方法相比，最优特征选择与序列化方法最多可将任务表现提升26.79%。同样，采用相关示例选择的上下文学习设置可将数据提取表现提升5.95%。基于研究结果，我们提出了若干准则，认为这些准则将有助于设计基于LLM的模型以支持健康搜索。
英文摘要：
Electronic Health Record (EHR) tables pose unique challenges among which is the presence of hidden contextual dependencies between medical features with a high level of data dimensionality and sparsity. This study presents the first investigation into the abilities of LLMs to comprehend EHRs for patient data extraction and retrieval. We conduct extensive experiments using the MIMICSQL dataset to explore the impact of the prompt structure, instruction, context, and demonstration, of two backbone LLMs, Llama2 and Meditron, based on task performance. Through quantitative and qualitative analyses, our findings show that optimal feature selection and serialization methods can enhance task performance by up to 26.79% compared to naive approaches. Similarly, in-context learning setups with relevant example selection improve data extraction performance by 5.95%. Based on our study findings, we propose guidelines that we believe would help the design of LLM-based models to support health search.
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。
