在医生笔记的高通量表型分析中，大型语言模型优于其他计算方法。
A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.
作者信息
Munzir Syed I, Hier Daniel B, Oommen Chelsea, Carrithers Michael D
机构信息
Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, USA.
Kummer Institute, Missouri University of Science and Technology, Rolla, MO, USA.
出版信息
AMIA Annu Symp Proc. 2025 May 22;2024:838-846. eCollection 2024.
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential for realizing value from electronic health records (EHR) in support of precision medicine. Despite technological advances, high-throughput phenotyping remains a challenge. This study compares three computational approaches to high-throughput phenotyping: a large language model (LLM) incorporating generative AI, a deep learning (DL) approach utilizing span categorization, and a machine learning (ML) approach with word embeddings. The LLM approach that implemented GPT-4 demonstrated superior performance, suggesting that large language models are poised to become the preferred method for high-throughput phenotyping ofphysician notes.
高通量表型分析，即将患者体征和症状自动映射到标准化本体概念，对于从电子健康记录（EHR）中实现价值以支持精准医学至关重要。尽管有技术进步，但高通量表型分析仍然是一项挑战。本研究比较了三种高通量表型分析的计算方法：一种结合生成式人工智能的大语言模型（LLM）、一种利用跨度分类的深度学习（DL）方法以及一种带有词嵌入的机器学习（ML）方法。实施GPT-4的LLM方法表现出卓越的性能，这表明大语言模型有望成为对医生记录进行高通量表型分析的首选方法。
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