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.

Details

Title
A metadata framework for computational phenotypes
Author
Spotnitz, Matthew 1   VIAFID ORCID Logo  ; Acharya, Nripendra 1 ; Cimino, James J 2 ; Murphy, Shawn 3 ; Namjou, Bahram 4 ; Crimmins, Nancy 4 ; Walunas, Theresa 5 ; Liu, Cong 1 ; Crosslin, David 6 ; Benoit, Barbara 7 ; Rosenthal, Elisabeth 8 ; Pacheco, Jennifer A 9 ; Ostropolets, Anna 1   VIAFID ORCID Logo  ; Harry Reyes Nieva 1   VIAFID ORCID Logo  ; Patterson, Jason S 1 ; Richter, Lauren R 1 ; Callahan, Tiffany J 1 ; Ahmed Elhussein 1 ; Pang, Chao 1 ; Kiryluk, Krzysztof 10 ; Jordan, Nestor 10   VIAFID ORCID Logo  ; Khan, Atlas 10 ; Mohan, Sumit 10 ; Minty, Evan 11 ; Chung, Wendy 12 ; Wei-Qi, Wei 13 ; Natarajan, Karthik 1 ; Weng, Chunhua 1 

 Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center , New York, New York, USA 
 Informatics Institute, Heersink School of Medicine, University of Alabama at Birmingham , Birmingham, Alabama, USA 
 Laboratory of Computer Science, Mass General Brigham , Boston, Massachusetts, USA 
 Department of Pediatrics, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA 
 Department of Medicine, Feinberg School of Medicine, Northwestern University , Chicago, Illinois, USA 
 Division of Biomedical Informatics and Genomics, Tulane University School of Medicine , New Orleans, Louisiana, USA 
 Department of Research Information Science & Computing, Mass General Brigham , Boston, Massachusetts, USA 
 Division of Genetics, University of Washington , Seattle, Washington, USA 
 Center for Genetic Medicine, Northwestern University , Chicago, Illinois, USA 
10  Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center , New York, New York, USA 
11  Department of Medicine, University of Calgary , Calgary, Alberta, Canada 
12  Department of Pediatrics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center , New York, New York, USA 
13  Department of Biomedical Informatics, Vanderbilt University , Nashville, Tennessee, USA 
Publication year
2023
Publication date
Jul 2023
Publisher
Oxford University Press
e-ISSN
25742531
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3168347472
Copyright
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.