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Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery
Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computationa...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6527418/ https://www.ncbi.nlm.nih.gov/pubmed/31119199 http://dx.doi.org/10.1038/s41746-019-0110-4 |
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author | Zhang, Xingmin Aaron Yates, Amy Vasilevsky, Nicole Gourdine, J. P. Callahan, Tiffany J. Carmody, Leigh C. Danis, Daniel Joachimiak, Marcin P. Ravanmehr, Vida Pfaff, Emily R. Champion, James Robasky, Kimberly Xu, Hao Fecho, Karamarie Walton, Nephi A. Zhu, Richard L. Ramsdill, Justin Mungall, Christopher J. Köhler, Sebastian Haendel, Melissa A. McDonald, Clement J. Vreeman, Daniel J. Peden, David B. Bennett, Tellen D. Feinstein, James A. Martin, Blake Stefanski, Adrianne L. Hunter, Lawrence E. Chute, Christopher G. Robinson, Peter N. |
author_facet | Zhang, Xingmin Aaron Yates, Amy Vasilevsky, Nicole Gourdine, J. P. Callahan, Tiffany J. Carmody, Leigh C. Danis, Daniel Joachimiak, Marcin P. Ravanmehr, Vida Pfaff, Emily R. Champion, James Robasky, Kimberly Xu, Hao Fecho, Karamarie Walton, Nephi A. Zhu, Richard L. Ramsdill, Justin Mungall, Christopher J. Köhler, Sebastian Haendel, Melissa A. McDonald, Clement J. Vreeman, Daniel J. Peden, David B. Bennett, Tellen D. Feinstein, James A. Martin, Blake Stefanski, Adrianne L. Hunter, Lawrence E. Chute, Christopher G. Robinson, Peter N. |
author_sort | Zhang, Xingmin Aaron |
collection | PubMed |
description | Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies. |
format | Online Article Text |
id | pubmed-6527418 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65274182019-05-20 Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery Zhang, Xingmin Aaron Yates, Amy Vasilevsky, Nicole Gourdine, J. P. Callahan, Tiffany J. Carmody, Leigh C. Danis, Daniel Joachimiak, Marcin P. Ravanmehr, Vida Pfaff, Emily R. Champion, James Robasky, Kimberly Xu, Hao Fecho, Karamarie Walton, Nephi A. Zhu, Richard L. Ramsdill, Justin Mungall, Christopher J. Köhler, Sebastian Haendel, Melissa A. McDonald, Clement J. Vreeman, Daniel J. Peden, David B. Bennett, Tellen D. Feinstein, James A. Martin, Blake Stefanski, Adrianne L. Hunter, Lawrence E. Chute, Christopher G. Robinson, Peter N. NPJ Digit Med Article Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies. Nature Publishing Group UK 2019-05-02 /pmc/articles/PMC6527418/ /pubmed/31119199 http://dx.doi.org/10.1038/s41746-019-0110-4 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zhang, Xingmin Aaron Yates, Amy Vasilevsky, Nicole Gourdine, J. P. Callahan, Tiffany J. Carmody, Leigh C. Danis, Daniel Joachimiak, Marcin P. Ravanmehr, Vida Pfaff, Emily R. Champion, James Robasky, Kimberly Xu, Hao Fecho, Karamarie Walton, Nephi A. Zhu, Richard L. Ramsdill, Justin Mungall, Christopher J. Köhler, Sebastian Haendel, Melissa A. McDonald, Clement J. Vreeman, Daniel J. Peden, David B. Bennett, Tellen D. Feinstein, James A. Martin, Blake Stefanski, Adrianne L. Hunter, Lawrence E. Chute, Christopher G. Robinson, Peter N. Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery |
title | Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery |
title_full | Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery |
title_fullStr | Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery |
title_full_unstemmed | Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery |
title_short | Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery |
title_sort | semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6527418/ https://www.ncbi.nlm.nih.gov/pubmed/31119199 http://dx.doi.org/10.1038/s41746-019-0110-4 |
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