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开发一种可计算的质母细胞瘤表型.

Sandra Yan1, Kaitlyn Melnick1, Xing He2,3

  • 1Department of Neurosurgery, College of Medicine, University of Florida, Gainesville, Florida, USA.

Neuro-oncology
|December 23, 2023
PubMed
概括

研究人员开发了一种可计算的表型 (CP),以准确地识别电子健康记录 (EHR) 中的多形质母细胞瘤 (GBM) 患者. 这种方法结合了结构化和非结构化数据,改善了对人口研究的患者鉴定.

关键词:
电子健康记录 (EHR) 是一种电子健康记录.可计算的现象型.质母细胞瘤 (glioblastoma) 是一种结构化数据是结构化数据.不结构化的数据.

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科学领域:

  • 医疗信息学 医疗信息学
  • 在瘤学瘤学.
  • 数据科学数据科学数据科学

背景情况:

  • 质母细胞瘤是最常见的恶性脑瘤.
  • 准确的患者鉴定对于人口研究至关重要.
  • 质母细胞瘤的诊断代码通常是不特定的,这给数据提取带来了挑战.

研究的目的:

  • 开发一种可计算的多形质母细胞瘤 (GBM) 的表型 (CP).
  • 利用来自电子健康记录 (EHR) 的结构化和非结构化数据.
  • 为了研究目的,改进GBM患者的鉴定.

主要方法:

  • 使用佛罗里达大学健康综合数据库.
  • 通过手动图表审查,对诊断代码,程序代码,药物代码和关键词进行代改进.
  • 评估多个拟议的CP以确定基于F1得分的最佳性能算法.

主要成果:

  • 进行了六轮手动图表审查,以改进CP元素.
  • 最优的CP规则结合了相关的诊断代码和关键词.
  • 选择的CP规则显示了使用结构化和非结构化数据的最高F1分数.

结论:

  • 开发了一种经过验证的CP算法来识别GBM患者.
  • 该算法有效地使用来自大型第三级护理中心的结构化和非结构化EHR数据.
  • 最终的算法获得了0.817的高F1得分,最大限度地减少了错误分类错误.