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现型执行和建模架构以支持疾病监测和现实世界的证据研究:英语哨兵网络评估
Gavin Jamie1, William Elson1, Debasish Kar1
1Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom.
JAMIA open
|May 13, 2024
概括
现型执行和建模架构 (PhEMA) 使用临床质量语言 (CQL) 和SNOMED CT FHIR值集来改善慢性疾病和监测的现型定义. 这种方法发现了更多的过度饮酒和类似流感的病例.
科学领域:
- 医疗信息学 医疗信息学
- 临床信息学 临床信息学
- 计算医学是一种计算医学.
背景情况:
- 开发标准化和可共享的临床表型对于研究和公共卫生监测至关重要.
- 现有的表型定义方法可能很复杂,缺乏互操作性.
- 越来越需要强大的架构来使用标准化语言和术语来表达表现型.
研究的目的:
- 评估表现型执行和建模架构 (PhEMA) 用于表达可共享的表现型.
- 使用临床质量语言 (CQL) 和密集的医学系统化命名法 (SNOMED) 临床术语 (CT) 快速医疗互操作性资源 (FHIR) 值集来定义现象型.
- 开发慢性疾病,社会人口统计学风险因素和监测的典范表型.
主要方法:
- 策划了三个表型:2型糖尿病 (T2DM),过度饮酒和发生的类似流感的疾病 (ILI).
- 定义的表型使用CQL用于临床和行政逻辑,以及SNOMED CT与FHIR值集.
- 将PhEMA的病例计数与现有方法进行比较,并根据已发表的表型希望数据进行评估.
主要成果:
- 使用SNOMED值集和CQL的T2DM表型定义略微增加了患病率 (7.2%至7.3%).
- 使用定性临床术语的过度饮酒表型定义,患病率增加了58% (1.2%至1.9%).
- 类似流感的疾病 (ILI) 现型定义与时间元素使用CQL增加了方便样本中的每周发病率.
结论:
- CQL和密集的FHIR值集提供了一种全面的方法来描述用于监视和研究的表型.
- 与以前的方法相比,PhEMA方法针对某些表型发现了更多病例.
- 新的工艺更好地满足了表型的需求,特别是在建模领域,需要进一步的工作来共享和存储.
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