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相关概念视频

Language and Cognition01:27

Language and Cognition

303
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
303

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相关实验视频

Updated: May 22, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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如何利用大型语言模型进行自动ICD编码?

Youngju Yoo1, Sewon Kim2

  • 1KAIST, Yuseong-gu, Daejeon, Republic of Korea; NAVER Digital Healthcare Lab, Seongnam-si, Gyeonggi-do, Republic of Korea; NAVER Cloud, Seongnam-si, Gyeonggi-do, Republic of Korea.

Computers in biology and medicine
|March 15, 2025
PubMed
概括
此摘要是机器生成的。

使用微调的大型语言模型 (LLM) 自动化国际疾病分类 (ICD) 编码提高了准确性. 这种新的框架提高了临床笔记分析和代码分配的LLM性能.

关键词:
临床说明 临床说明微调的微调.ICD编码是指ICD的编码.大型语言模型

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 准确的国际疾病分类 (ICD) 编码对于医疗管理,研究和索赔处理至关重要.
  • 手动ICD编码是低效的,容易出错,并与临床笔记固有的复杂性和噪音作斗争.
  • 现有的自动化方法面临挑战,因为标签空间广,ICD代码分布长尾.

研究的目的:

  • 开发和评估大型语言模型 (LLM) 的新型微调框架,以自动化ICD编码.
  • 解决LLMs在处理杂的临床文本和处理ICD代码分配的复杂性方面的局限性.
  • 提高医疗保健机构自动ICD编码的准确性和效率.

主要方法:

  • 提出了专门为自动ICD编码而设计的LLM的微调框架.
  • 整合了标签注意机制,以专注于相关的诊断标签.
  • 整合了与笔记相关的医学知识,并采用了基于知识的采样来管理LLM输入令牌约束.

主要成果:

  • 与MIMIC-III-50数据集上的香草微调相比,拟议的框架显示出更高的性能.
  • 观察到微观和宏观准确度以及F1分数的显著改善.
  • 编码器-解码器LLM架构在增强的微调方法中表现出特别显著的收益.

结论:

  • 新的微调框架有效地增强了自动ICD编码的LLM能力.
  • 该框架成功地解决了临床笔记特征和ICD代码分布所带来的挑战.
  • 这种方法为临床实践中准确和高效的自动化ICD编码提供了一个有希望的解决方案.