双重目标自主监督集群与多功能融合,用于医疗问题文本
Xifeng Shen1, Yuanyuan Sun2, Chunxia Zhang3
1Institute of Medical Information, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
PeerJ. Computer science
|December 13, 2024
概括
本研究介绍了一种用于医学文本集群的多特征融合双目标自我监督集群 (MF-DSC) 模型. 与现有方法相比,MF-DSC显著提高了医疗问题聚类的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 医学问答数据存在一些挑战,原因是文本结构不整,字符限制短,语言不规则.
- 单一特征提取模型难以完全捕捉医学文本的内容.
研究的目的:
- 为医疗问题开发一个端到端的文本集群模型.
- 通过多功能融合,增强医学文本中的信息表示.
主要方法:
- 提出了一种多功能融合双目标自主监督集群 (MF-DSC) 模型.
- 集成术语频率和词汇语义,生成加权的词向量,用于深度学习.
- 采用了自我注意机制和两个目标功能 (L集群,L主题) 来实现统一的集群.
主要成果:
- 与其他五种模型相比,MF-DSC表现出优越的性能.
- 在规范化的相互信息 (0.4346) 中获得高分,调整后的兰德指数 (0.4934),平均集群精度 (0.8649) 和F1 (0.5737) 中获得高分.
结论:
- 在MF-DSC模型有效集群医疗文本.
- 多功能融合和自主监督学习增强了医疗问题数据的表示和聚类.
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