A metadata framework for computational phenotypes

Lay Summary Computational phenotypes are essential to scale precision medicine and observational healthcare research. More comprehensive and explicitly defined phenotype metadata could improve phenotype retrieval, reuse, and sharing. However, few studies have focused directly on phenotype metadata explicitness or validation methods and metrics. We designed a phenotype metadata framework as part of ongoing research with the Electronic Medical Records and Genomics (eMERGE) network phenotyping working group. We identified 39 metadata elements based on group consensus. We distributed a survey to 47 new researchers that rated the usefulness of each metadata element on a scale of 1-5, and conducted a thematic analysis of the free-text survey questions. Two researchers annotated 8 type-2 diabetes mellitus phenotypes with the framework. More than 90% of respondents assigned a rating of 4-5 to metadata framework elements regarding phenotype definition and validation metrics. In our thematic analysis, explicit descriptions, compliance with data standards, and comprehensive validation methods were strengths of the framework. Using a mixed-methods approach, we have developed a comprehensive framework for defining computational clinical phenotypes. Use of this framework may help curate patient data used for both observational and prospective healthcare research. 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.

Automated summary

This study develops a comprehensive metadata framework for defining computational clinical phenotypes. The framework was positively evaluated by more than 90% of the survey respondents in terms of phenotype definition and validation methods and metrics. The strengths of the framework include explicit descriptions, compliance with data standards, and comprehensive validation methods.