在远程医疗的背景下,在高度不平衡的多类分布中进行专业检测
Alaa Alomari1,2, Hossam Faris1,2, Pedro A Castillo1
1University of Granada, Granada, Spain.
PloS one
|November 16, 2023
概括
这项研究引入了一种机器学习模型,可以自动从阿拉伯语问题中检测医疗专业,从而提高远程医疗的效率. 结合SMOTE和重权重等技术可以提高准确性,特别是在不平衡数据集中的罕见情况.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 随着COVID-19大流行,远程医疗需求增加,需要自动化来管理运营负载.
- 自动化医疗专业检测对于有效的患者路由和个性化咨询至关重要.
研究的目的:
- 为阿拉伯医学问题开发基于机器学习的专业检测分类器.
- 解决医疗专业分类中的多类和不平衡数据集的挑战.
- 探索专业检测在定制咨询流程中的业务应用.
主要方法:
- 开发了一个深度神经网络 (DNN) 模型用于专业检测.
- 比较过量采样技术 (例如,SMOTE) 和重新权衡不平衡数据.
- 集成的关键词识别与机器学习模型.
- 在同步和异步远程医疗咨询中部署分类器.
主要成果:
- 拟议的模型有效地处理多类和不平衡的阿拉伯医学问题数据集.
- 结合SMOTE,重权和关键词识别,改善了罕见类的检测.
- 该系统在定制不同专业的咨询流程方面表现出灵活性.
结论:
- 机器学习,特别是DNN,可以在远程医疗中自动化医学专业检测.
- 有效处理不平衡的数据对于准确分类罕见疾病至关重要.
- 自动化专科检测提高了远程医疗的效率和个性化.
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