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使用基于树的扫描统计方法进行电子健康记录增强的信号检测.

Massimiliano Russo1,2, Sushama Kattinakere Sreedhara2, Joshua Smith3

  • 1Department of Statistics, The Ohio State University, Columbus, OH, United States.

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概括

增强了基于树的扫描统计 (TBSS) 方法,以分析电子健康记录 (EHR),检测与糖尿病药物相关的头痛等不良药物影响.

关键词:
数据挖掘是数据挖掘的一个方法.电子健康记录是电子医疗记录.自然语言处理自然语言处理.调配试验测试 调配试验测试 调配试验测试药学流行病学 药学流行病学现实世界的数据.基于树的扫描统计数据.

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科学领域:

  • 药物监督 药物监督 药物监督
  • 数据挖掘 数据挖掘
  • 医疗信息学 医疗信息学

背景情况:

  • 基于树的扫描统计 (TBSS) 传统上使用来自索赔数据的诊断代码来检测药物不良影响.
  • 尚未探索TBSS应用于丰富的电子健康记录 (EHR) 数据,包括临床笔记和实验室结果.
  • 整合不同的电子健康数据源可能会提高TBSS检测安全信号的灵敏度.

研究的目的:

  • 开发和评估将EHR数据集成到TBSS分析中的方法.
  • 评估TBSS与各种EHR数据源的实用性,以检测不良药物事件.
  • 为了比较使用诊断代码与NLP衍生结果的TBSS的性能,实验室结果 (二进制和连续).

主要方法:

  • 开发了新的方法来将EHR数据,包括自然语言处理 (NLP) 结果和实验室结果纳入TBSS.
  • 分析了第二代硫尿素 (SUs) 和双基酶4 (DPP-4) 抑制剂在2型糖尿病成年人中的比较队列研究的数据.
  • 顺序添加到TBSS模型的数据源:诊断代码,NLP结果,二进制实验室结果和连续实验室结果.

主要成果:

  • 仅仅通过诊断代码,就无法在住院或急诊环境中发出与低血糖相关事件的警报.
  • 结合NLP衍生的结果,确定"头痛"是潜在的安全信号 (P = .047),这是低血糖症的非特异性症状.
  • 添加二进制和连续实验室结果逐渐证实了"头痛"警报,证明了信号检测能力的提高.

结论:

  • 整合EHR数据,特别是NLP衍生的结果和实验室结果,显著提高了TBSS检测潜在不良药物事件的能力.
  • 适应EHR数据的TBSS可以成为识别传统索赔数据分析可能错过的安全信号的宝贵工具.
  • 这种方法对积极的药物监测和改善现实世界临床环境中的患者安全具有前性.