A metadata framework for computational phenotypes.
A metadata framework for computational phenotypes.
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随着计算表型的迅速发展，为正确的任务识别正确的表型变得越来越困难。本研究使用混合方法来开发和评估用于检索和重用计算表型的新型元数据框架。来自电子病历和基因组学以及观察性健康数据科学和信息学两个大型研究网络的20名活跃的表型研究人员被招募来建议元数据元素。一旦在39个元数据元素上达成共识，就会对47名新的研究人员进行调查，以评估元数据框架的效用。调查包括5-Likert多项选择题和开放式问题。另外两名研究人员被要求使用元数据框架来注释8种2型糖尿病表型。超过90%的受访者对有关表型定义、验证方法和度量的元数据元素给予正面评价，得分为4或5分。两名研究人员在60分钟内完成了每种表型的注释。我们对叙事反馈的专题分析表明，元数据框架在捕获丰富而明确的描述、实现表型搜索、符合数据标准和综合验证指标方面是有效的。目前的限制是数据收集的复杂性和所需的人力成本。
With the burgeoning development of computational phenotypes, it is increasingly difficult to identify the right phenotype for the right tasks. This study uses a mixed-methods approach to develop and evaluate a novel metadata framework for retrieval of and reusing computational phenotypes. Twenty active phenotyping researchers from 2 large research networks, Electronic Medical Records and Genomics and Observational Health Data Sciences and Informatics, were recruited to suggest metadata elements. Once consensus was reached on 39 metadata elements, 47 new researchers were surveyed to evaluate the utility of the metadata framework. The survey consisted of 5-Likert multiple-choice questions and open-ended questions. Two more researchers were asked to use the metadata framework to annotate 8 type-2 diabetes mellitus phenotypes. More than 90% of the survey respondents rated metadata elements regarding phenotype definition and validation methods and metrics positively with a score of 4 or 5. Both researchers completed annotation of each phenotype within 60 min. Our thematic analysis of the narrative feedback indicates that the metadata framework was effective in capturing rich and explicit descriptions and enabling the search for phenotypes, compliance with data standards, and comprehensive validation metrics. Current limitations were its complexity for data collection and the entailed human costs.
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DOI：
10.1136/amiajnl-2012-000896
发表时间：
2013-06-01
影响因子：
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作者：
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DOI：
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