WangLab在MEDIQA-Chat 2023:使用大型语言模型从医患对话中生成临床笔记

John Giorgi, Augustin Toma, Ronald Xie, Sondra S. Chen, Kevin R. An, Grace X. Zheng, Bo Wang
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引用次数: 2

摘要

本文描述了我们提交给MEDIQA-Chat 2023的共享任务,用于从医患对话中自动生成临床记录。我们报告了两种方法的结果:第一种方法对共享任务数据上的预训练语言模型(PLM)进行微调,第二种方法使用带有大型语言模型(LLM)的少镜头上下文学习(ICL)。通过自动度量(例如ROUGE, BERTScore),两者都实现了高性能,并且在所有提交给共享任务的提交中分别排名第二和第一。专家的审查表明,通过基于icl的方法与GPT-4生成的笔记与人类编写的笔记一样受欢迎,这使得它成为医患对话自动生成笔记的有希望的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models
This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approaches: the first fine-tunes a pre-trained language model (PLM) on the shared task data, and the second uses few-shot in-context learning (ICL) with a large language model (LLM). Both achieve high performance as measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and first, respectively, of all submissions to the shared task. Expert human scrutiny indicates that notes generated via the ICL-based approach with GPT-4 are preferred about as often as human-written notes, making it a promising path toward automated note generation from doctor-patient conversations.
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