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当原始数据占优势时:大型语言模型嵌入是否有效用于医疗机器学习应用程序的数字数据表示?

Yanjun Gao^1,2, Skatje Myers², Shan Chen^3,4

¹University of Colorado.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing

|December 4, 2025

概括

大型语言模型 (LLM) 在分析医疗任务的电子健康记录 (EHR) 中表现有前途. 虽然原始数据目前表现更好,但LLM嵌入式为诊断和预后提供了竞争力的结果.

科学领域:

人工智能的人工智能
医疗信息学医疗信息学
机器学习机器学习

背景情况:

大型语言模型 (LLM) 已经进行了先进的数据分析,但它们与表式临床数据的使用仍未得到充分探索.
电子健康记录 (EHR) 包含医疗诊断和预后的关键数字数据.

研究的目的:

通过使用EHR数据来评估医学任务的最后隐藏状态的LLM嵌入.
将LLM嵌入与原始数值EHR数据进行比较,作为传统机器学习模型的特征.

主要方法:

在零射击设置中利用调整指令的LLM来表示异常的生理数据.
采用极端梯度提升 (XGBoost) 作为传统的机器学习基准.
研究了用于零射击和少数射击LLM嵌入的提示工程技术.

主要成果:

原始的数值EHR数据通常在医疗机器学习任务中表现优于LLM嵌入.
零射击的LLM嵌入显示出竞争性表现,表明潜在的实用性.
快速工程影响了LLM嵌入的有效性.

结论:

在医学ML中,LLM嵌入提供了一个有希望的,虽然尚未优越的,在医学ML中提取特征的方法.
需要进一步的研究来优化LLM与EHR数据的集成,以获得临床应用.

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