The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical data pivotal in clinical contexts, into LLM paradigms has not been thoroughly explored. In this study, we examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record (EHR) data. We compare the performance of these embeddings with that of raw numerical EHR data when used as feature inputs to traditional machine learning (ML) algorithms that excel at tabular data learning, such as eXtreme Gradient Boosting. We focus on instruction-tuned LLMs in a zero-shot setting to represent abnormal physiological data and evaluating their utilities as feature extractors to enhance ML classifiers for predicting diagnoses, length of stay, and mortality. Furthermore, we examine prompt engineering techniques on zero-shot and few-shot LLM embeddings to measure their impact comprehensively. Although findings suggest the raw data features still prevails in medical ML tasks, zero-shot LLM embeddings demonstrate competitive results, suggesting a promising avenue for future research in medical applications.

本研究针对在医疗场景中，如何有效集成表格数值数据与大型语言模型（LLM）进行探讨，填补了该领域的研究空白。通过比较LLM的最后隐藏状态生成的向量表示和原始数值电子健康记录数据在传统机器学习算法中的表现，发现尽管原始数据仍具优势，LLM嵌入在医疗预测任务中同样具备竞争力，指向了未来研究的新方向。

原始数据的胜利：大型语言模型嵌入在医疗机器学习应用中的数值数据表示是否有效？