A metadata framework for computational phenotypes.

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES
JAMIA Open Pub Date : 2023-05-09 eCollection Date: 2023-07-01 DOI:10.1093/jamiaopen/ooad032
Matthew Spotnitz, Nripendra Acharya, James J Cimino, Shawn Murphy, Bahram Namjou, Nancy Crimmins, Theresa Walunas, Cong Liu, David Crosslin, Barbara Benoit, Elisabeth Rosenthal, Jennifer A Pacheco, Anna Ostropolets, Harry Reyes Nieva, Jason S Patterson, Lauren R Richter, Tiffany J Callahan, Ahmed Elhussein, Chao Pang, Krzysztof Kiryluk, Jordan Nestor, Atlas Khan, Sumit Mohan, Evan Minty, Wendy Chung, Wei-Qi Wei, Karthik Natarajan, Chunhua Weng
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Abstract

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.

Abstract Image

计算表型的元数据框架。
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开发和评估一种新的元数据框架,用于检索和重用计算表型。来自电子病历和基因组学以及观测健康数据科学和信息学两个大型研究网络的20名活跃表型研究人员被招募来提出元数据元素。一旦就39个元数据元素达成共识,就对47名新的研究人员进行了调查,以评估元数据框架的效用。这项调查包括5个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%的调查受访者对表型定义、验证方法和指标的元数据元素给予积极评价,得分为4或5。两位研究人员在60秒内完成了对每个表型的注释 min。我们对叙述性反馈的主题分析表明,元数据框架在捕捉丰富而明确的描述方面是有效的,并能够搜索表型、遵守数据标准和全面的验证指标。目前的局限性在于其数据收集的复杂性和所需的人力成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
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