减轻医疗压力:通过跨领域文本分类进行分类方法
Xiao Xiao1, Shuqin Wang2, Feng Jiang3
1Department of Ultrasound, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China.
Frontiers in computational neuroscience
|January 2, 2025
概括
这项研究引入了一种用于患者分拣的新快速调整方法,克服了数据限制并提高了分类准确性. 该方法有效地引导患者到适当的医院部门,提高医疗保健的效率.
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.
- 人工智能的人工智能
背景情况:
- 患者经常难以确定正确的医院部门,导致延误和低效的护理.
- 现有的人工智能分类方法需要大量的数据,可能无法保存语义结构,从而限制了它们的有效性.
研究的目的:
- 开发一种新的跨领域文本分类方法,用于准确的患者分类.
- 解决现有方法的局限性,包括数据依赖性和语义特征扭曲.
主要方法:
- 使用快速调整方法来对患者的症状描述进行分类.
- 提示模板的手工制作包含源域信息.
- 五种策略扩大标签词空间,以便快速修改,形成最终的语言化器.
主要成果:
- 拟议的方法在中国选数据集上实现了最先进的性能.
- 在对患者文本进行分类,以获得适当的部门建议方面,已证明有效.
- 成功克服了数据可访问性和语义特征扭曲性的限制.
结论:
- 提示调节方法为自动化患者分拣提供了有效的解决方案.
- 这种方法通过准确地引导患者来提高医疗保健的效率.
- 该方法对医学中类似的跨领域分类任务具有前景.
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