基于规则的电子健康记录算法的性能特征,用于识别严重和微观血患者
Jasmine Kashkoush1, Mudit Gupta2, Matthew A Meissner1
1Department of Urology, Geisinger, Danville, Pennsylvania, United States.
Methods of information in medicine
|September 4, 2023
概括
一个新的算法准确地从电子健康记录中识别尿液中的血液 (血) 病例. 这个工具有助于了解患者护理模式,以评估血.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 血每年影响200万患者,需要有效的评估方法.
- 美国泌尿病学协会的指导方针建议在血评估时进行风险分层.
- 将CT扫描等高级成像技术限制在高风险患者身上是一个关键建议.
研究的目的:
- 在电子健康记录 (EHR) 中开发和验证用于识别血病例的算法.
- 使用EHR数据,区分粗血和微观血.
- 为了能够对血评估模式进行人口层面的分析.
主要方法:
- 使用国际疾病分类 (ICD) -9/ICD-10代码,尿液颜色和显微镜数据.
- 通过代过程和图表审查开发和完善算法.
- 将验证的算法应用于大量成年患者队列 (n=539,516).
主要成果:
- 鉴定出51500例血病例和488016例对照病例,准确度很高 (PPV为100%,NPV为99%).
- 成功分类的血类型: 11,435 个粗, 26,658 个显微, 12,562 个不确定的.
- 总血算法显示100%的PPV和99%的NPV;微观血算法显示78%的PPV和100%的NPV.
结论:
- 基于EHR的算法准确地识别和分类出血 (尿液中的血液).
- 这种工具有助于研究血液的护理模式.
- 该算法支持理解和潜在地改善评估这种常见的泌尿病状况.
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