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相关概念视频

Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.0K

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相关实验视频

Updated: Sep 14, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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使用大型语言模型进行传统中医公式分类的权衡投票方法:算法开发和验证研究

Zhe Wang1,2, Keqian Li3, Suyuan Peng4

  • 1Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences; School of Basic Medicine, Peking Union Medical College, Beijing, China.

JMIR medical informatics
|July 24, 2025
PubMed
概括

这项研究表明,使用大语言模型 (LLM) 进行集体学习显著提高了传统中医 (TCM) 公式分类准确度. 最好的组合模型实现了77.15%的准确性,增强了TCM知识发现.

关键词:
在TCM配方分类的分类中,算法开发开发的发展算法组合学习组合学习大型语言模型.传统的中国医药传统的中国医药.

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Preparation of Gynura bicolor DC samples for High-Resolution Tandem Mass Spectrometry
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科学领域:

  • 生物医学信息学 生物医学信息学
  • 计算语言学 计算语言学
  • 传统中国医药 传统中国医药

背景情况:

  • 传统中医药 (TCM) 配方具有关于成分,疗效和适用情况的关键信息.
  • 分类TCM公式对于标准化,临床支持,研究和现代化至关重要.
  • 大型语言模型 (LLM) 为增强TCM知识发现提供了高级功能.

研究的目的:

  • 评估各种LLM在分类TCM公式中的表现.
  • 提高TCM公式分类准确度,使用集体学习与微调的LLMs.

主要方法:

  • 手动策划和清理2441个TCM公式的数据集.
  • 精心调整了10个中国支持的LLM.
  • 雇员集体学习与硬和加权的投票机制.
  • 精选出表现最好的模特进行组合投票 (前5名和前3名).

主要成果:

  • Qwen-14B实现了最高的单模准确率75.32%.
  • 组合方法提高了准确性:硬投票 (75.79%),加权投票 (76.47%),加权投票 (前五名) (75.57%) 和加权投票 (前三名) (77.15%).

结论:

  • 与LLM一起学习有效地提高了TCM公式分类的准确性.
  • 拟议的方法改进了TCM配方疗效的分类系统.
  • 这种方法加速了TCM知识的发现,并促进了科学应用.