长期COVID的分类从全科医生诊断文本
Abbish Kamalakkannan1, Gorkem Sezgin1, Adam McLeod2
1Australian Institute of Health Innovation, Macquarie University, Australia.
Studies in health technology and informatics
|June 22, 2023
概括
从医生那里识别长期COVID患者
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 临床研究 临床研究
背景情况:
- 准确识别长期COVID患者对于了解病情至关重要.
- 来自全科医生的非结构化诊断文本对患者识别构成了重大挑战.
研究的目的:
- 提出一个分类框架,以从非结构化的诊断文本中识别长期COVID患者.
- 为了更好地了解澳大利亚的长期COVID风险因素,管理和结果.
主要方法:
- 开发一个分类框架.
- 适用于非结构化诊断文本.
- 鉴定长期COVID患者队列的分析.
主要成果:
- 开发了一个功能分类框架,用于识别长期COVID患者.
- 该框架有助于从全科医生记录中分析患者数据.
结论:
- 拟议的框架为在初级保健机构中识别长期COVID患者提供了可行的解决方案.
- 这种方法可以支持对澳大利亚长期COVID流行病学,治疗和患者结果的研究.
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