改进与COVID相关的长文本分类:一个新的端到端域适应性重述框架
Sai Ashish Somayajula1, Onkar Litake1, Youwei Liang1
1Department of Electrical and Computer Engineering, University of California, La Jolla, San Diego, USA.
Scientific reports
|January 3, 2024
概括
这项研究引入了一种新的机器学习方法来对长期的COVID文献进行分类,通过医学转述和独特的优化框架来克服数据稀缺性. 该方法提高了研究人员浏览复杂的COVID-19信息的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 由于不断发展的科学和不一致的术语,长期COVID文献的分类具有挑战性.
- 对于医疗保健专业人员和科学家来说,很难确定长期COVID的相关研究.
- 标注数据的稀缺性阻碍了该领域的机器学习应用.
研究的目的:
- 开发一种有效的机器学习方法来分类长期的COVID文献.
- 为应对培训分类模型有限的注释数据的挑战.
- 为了提高长期COVID研究文献检索的准确性和效率.
主要方法:
- 采用机器学习技术进行文献分类.
- 引入了医学转述,以使训练数据多样化,同时保留内容.
- 提出了一个基于数据重权重的多层次优化框架,使用一个元权重网络 (MWN).
- 来自下游文本分类模型的综合反以指导转述模型培训.
主要成果:
- 拟议的框架显著提高了长期COVID文献分类的准确性.
- 该方法在对相关科学文本进行分类方面表现出更高的效率.
- 这种方法有效地解决了缺乏注释数据的障碍.
结论:
- 开发的机器学习策略为医生和研究人员提供了宝贵的工具.
- 这种方法有助于在复杂而不断变化的长期COVID研究领域进行导航.
- 医疗转述与域自适应优化相结合,增强了分类任务.
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