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加强传统中医诊断:整合多标签症状实体分类的本体知识

Hangle Hu¹, Chunlei Cheng^1,2, Qing Ye¹

¹School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang 330004, China.

Mathematical biosciences and engineering : MBE

|February 2, 2024

概括

这项研究引入了用于传统中医 (TCM) 症状提取的AI模型,提高了综合征区分的准确性. 这种新的方法通过整合知识图和高级实体识别来增强TCM诊断.

科学领域:

人工智能在医学中的应用
传统中国医学 (TCM) 是一种

背景情况:

在准确的症状识别和分类方面,TCM综合征的分化面临着挑战.
现有的人工智能模型在有限的标签空间和不足的领域知识集成方面扎.

研究的目的:

为TCM症状开发一个多标签实体提取模型.
通过整合领域知识来解决TCM当前实体识别模型的局限性.

主要方法:

开发了一个模型,将知识图与TCM症状本体学结合起来.
从变压器 (BERT) + 双向长短期存储器 (Bi-LSTM) + 有条件随机字段 (CRF) 的综合双向编码器表示,具有多标签分类.
引入了一个多关联的特征融合模块,用于增强特征提取和标签相互关系辨别.

主要成果:

该模型在多标签症状提取方面表现出卓越的性能.
在TCM症状分类的效率和准确性方面取得了显著的改进.
在文本数据中成功地导航了复杂的标签相互依赖.

结论:

拟议的AI模型显著推进了TCM综合征分化和疾病诊断.
知识图和先进的人工智能技术的整合增强了TCM知识的标准化和丰富.
这项研究为未来的TCM人工智能应用提供了坚实的基础.

关键词:

人工智能的人工智能是人工智能.生物医学系统分析分析医疗信息学是一门医学信息学专业.多标签实体提取多标签实体提取自然语言处理自然语言处理.

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