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从电子健康记录分类连续葡萄糖监测文件
Yaguang Zheng1, Eduardo Iturrate2, Lehan Li3
1Rory Meyers College of Nursing, New York University, New York, NY, USA.
Journal of diabetes science and technology
|March 12, 2025
概括
在电子健康记录 (EHR) 中准确分类持续葡萄糖监测 (CGM) 门诊葡萄糖概况 (AGP) 报告至关重要. 一个自动化算法在识别AGP报告时实现了高灵敏度 (95.0%) 和特异性 (91.7%).
科学领域:
- 医疗信息学 医疗信息学
- 糖尿病 技术 技术
背景情况:
- 持续葡萄糖监测 (CGM) 的临床使用越来越多,导致电子健康记录 (EHR) 中更多的CGM文档.
- 在EHR中存储CGM数据缺乏标准化,这给数据检索和分析带来了挑战.
研究的目的:
- 评估CGM门诊葡萄糖概况 (AGP) 报告的分类标准的敏感性和特异性.
- 评估自动化算法在区分AGP报告与其他CGM相关文档中的准确性.
主要方法:
- 开发了一个文档分类算法,包括图像处理,光学字符识别和关键字分析.
- 该算法处理了2244份文件,将它们分类为CGM AGP报告,非CGM报告或不确定.
- 两名专家对一组文件 (62份报告) 进行了手动审查,以验证算法的性能,计算灵敏度和特异性.
主要成果:
- 在2244份文件中,46.5%被确定为CGM AGP报告,52.1%是其他临床文件.
- 专家审查人员在文档评估中实现了100%的敏感性和98.4%的特异性.
- 与手动审查相比,自动分类算法表现出95.0%的灵敏度和91.7%的特异性.
结论:
- 在电子健康记录中,大约有一半的CGM相关文档是AGP报告,对临床实践和研究有价值.
- 另一半由各种非AGP临床文件组成,突出显示存储的CGM数据的异质性.
- 为了改进数据管理和利用,需要对电子健康记录中CGM文档存储的标准化.
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