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Impact of Diverse Data Sources on Computational Phenotyping
Electronic health records (EHRs) are widely adopted with a great potential to serve as a rich, integrated source of phenotype information. Computational phenotyping, which extracts phenotypes from EHR data automatically, can accelerate the adoption and utilization of phenotype-driven efforts to adva...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7283539/ https://www.ncbi.nlm.nih.gov/pubmed/32582289 http://dx.doi.org/10.3389/fgene.2020.00556 |
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author | Wang, Liwei Olson, Janet E. Bielinski, Suzette J. St. Sauver, Jennifer L. Fu, Sunyang He, Huan Cicek, Mine S. Hathcock, Matthew A. Cerhan, James R. Liu, Hongfang |
author_facet | Wang, Liwei Olson, Janet E. Bielinski, Suzette J. St. Sauver, Jennifer L. Fu, Sunyang He, Huan Cicek, Mine S. Hathcock, Matthew A. Cerhan, James R. Liu, Hongfang |
author_sort | Wang, Liwei |
collection | PubMed |
description | Electronic health records (EHRs) are widely adopted with a great potential to serve as a rich, integrated source of phenotype information. Computational phenotyping, which extracts phenotypes from EHR data automatically, can accelerate the adoption and utilization of phenotype-driven efforts to advance scientific discovery and improve healthcare delivery. A list of computational phenotyping algorithms has been published but data fragmentation, i.e., incomplete data within one single data source, has been raised as an inherent limitation of computational phenotyping. In this study, we investigated the impact of diverse data sources on two published computational phenotyping algorithms, rheumatoid arthritis (RA) and type 2 diabetes mellitus (T2DM), using Mayo EHRs and Rochester Epidemiology Project (REP) which links medical records from multiple health care systems. Results showed that both RA (less prevalent) and T2DM (more prevalent) case selections were markedly impacted by data fragmentation, with positive predictive value (PPV) of 91.4 and 92.4%, false-negative rate (FNR) of 26.6 and 14% in Mayo data, respectively, PPV of 97.2 and 98.3%, FNR of 5.2 and 3.3% in REP. T2DM controls also contain biases, with PPV of 91.2% and FNR of 1.2% for Mayo. We further elaborated underlying reasons impacting the performance. |
format | Online Article Text |
id | pubmed-7283539 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72835392020-06-23 Impact of Diverse Data Sources on Computational Phenotyping Wang, Liwei Olson, Janet E. Bielinski, Suzette J. St. Sauver, Jennifer L. Fu, Sunyang He, Huan Cicek, Mine S. Hathcock, Matthew A. Cerhan, James R. Liu, Hongfang Front Genet Genetics Electronic health records (EHRs) are widely adopted with a great potential to serve as a rich, integrated source of phenotype information. Computational phenotyping, which extracts phenotypes from EHR data automatically, can accelerate the adoption and utilization of phenotype-driven efforts to advance scientific discovery and improve healthcare delivery. A list of computational phenotyping algorithms has been published but data fragmentation, i.e., incomplete data within one single data source, has been raised as an inherent limitation of computational phenotyping. In this study, we investigated the impact of diverse data sources on two published computational phenotyping algorithms, rheumatoid arthritis (RA) and type 2 diabetes mellitus (T2DM), using Mayo EHRs and Rochester Epidemiology Project (REP) which links medical records from multiple health care systems. Results showed that both RA (less prevalent) and T2DM (more prevalent) case selections were markedly impacted by data fragmentation, with positive predictive value (PPV) of 91.4 and 92.4%, false-negative rate (FNR) of 26.6 and 14% in Mayo data, respectively, PPV of 97.2 and 98.3%, FNR of 5.2 and 3.3% in REP. T2DM controls also contain biases, with PPV of 91.2% and FNR of 1.2% for Mayo. We further elaborated underlying reasons impacting the performance. Frontiers Media S.A. 2020-06-03 /pmc/articles/PMC7283539/ /pubmed/32582289 http://dx.doi.org/10.3389/fgene.2020.00556 Text en Copyright © 2020 Wang, Olson, Bielinski, St. Sauver, Fu, He, Cicek, Hathcock, Cerhan and Liu. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wang, Liwei Olson, Janet E. Bielinski, Suzette J. St. Sauver, Jennifer L. Fu, Sunyang He, Huan Cicek, Mine S. Hathcock, Matthew A. Cerhan, James R. Liu, Hongfang Impact of Diverse Data Sources on Computational Phenotyping |
title | Impact of Diverse Data Sources on Computational Phenotyping |
title_full | Impact of Diverse Data Sources on Computational Phenotyping |
title_fullStr | Impact of Diverse Data Sources on Computational Phenotyping |
title_full_unstemmed | Impact of Diverse Data Sources on Computational Phenotyping |
title_short | Impact of Diverse Data Sources on Computational Phenotyping |
title_sort | impact of diverse data sources on computational phenotyping |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7283539/ https://www.ncbi.nlm.nih.gov/pubmed/32582289 http://dx.doi.org/10.3389/fgene.2020.00556 |
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