It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
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.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details




1 Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center , New York, New York, USA
2 Informatics Institute, Heersink School of Medicine, University of Alabama at Birmingham , Birmingham, Alabama, USA
3 Laboratory of Computer Science, Mass General Brigham , Boston, Massachusetts, USA
4 Department of Pediatrics, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA
5 Department of Medicine, Feinberg School of Medicine, Northwestern University , Chicago, Illinois, USA
6 Division of Biomedical Informatics and Genomics, Tulane University School of Medicine , New Orleans, Louisiana, USA
7 Department of Research Information Science & Computing, Mass General Brigham , Boston, Massachusetts, USA
8 Division of Genetics, University of Washington , Seattle, Washington, USA
9 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