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A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease
BACKGROUND: Data collected in EHRs have been widely used to identifying specific conditions; however there is still a need for methods to define comorbidities and sources to identify comorbidities burden. We propose an approach to assess comorbidities burden for a specific disease using the literatu...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5622531/ https://www.ncbi.nlm.nih.gov/pubmed/28962565 http://dx.doi.org/10.1186/s12911-017-0537-y |
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author | Escudié, Jean-Baptiste Rance, Bastien Malamut, Georgia Khater, Sherine Burgun, Anita Cellier, Christophe Jannot, Anne-Sophie |
author_facet | Escudié, Jean-Baptiste Rance, Bastien Malamut, Georgia Khater, Sherine Burgun, Anita Cellier, Christophe Jannot, Anne-Sophie |
author_sort | Escudié, Jean-Baptiste |
collection | PubMed |
description | BACKGROUND: Data collected in EHRs have been widely used to identifying specific conditions; however there is still a need for methods to define comorbidities and sources to identify comorbidities burden. We propose an approach to assess comorbidities burden for a specific disease using the literature and EHR data sources in the case of autoimmune diseases in celiac disease (CD). METHODS: We generated a restricted set of comorbidities using the literature (via the MeSH® co-occurrence file). We extracted the 15 most co-occurring autoimmune diseases of the CD. We used mappings of the comorbidities to EHR terminologies: ICD-10 (billing codes), ATC (drugs) and UMLS (clinical reports). Finally, we extracted the concepts from the different data sources. We evaluated our approach using the correlation between prevalence estimates in our cohort and co-occurrence ranking in the literature. RESULTS: We retrieved the comorbidities for 741 patients with CD. 18.1% of patients had at least one of the 15 studied autoimmune disorders. Overall, 79.3% of the mapped concepts were detected only in text, 5.3% only in ICD codes and/or drugs prescriptions, and 15.4% could be found in both sources. Prevalence in our cohort were correlated with literature (Spearman’s coefficient 0.789, p = 0.0005). The three most prevalent comorbidities were thyroiditis 12.6% (95% CI 10.1–14.9), type 1 diabetes 2.3% (95% CI 1.2–3.4) and dermatitis herpetiformis 2.0% (95% CI 1.0–3.0). CONCLUSION: We introduced a process that leveraged the MeSH terminology to identify relevant autoimmune comorbidities of the CD and several data sources from EHRs to phenotype a large population of CD patients. We achieved prevalence estimates comparable to the literature. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-017-0537-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5622531 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56225312017-10-11 A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease Escudié, Jean-Baptiste Rance, Bastien Malamut, Georgia Khater, Sherine Burgun, Anita Cellier, Christophe Jannot, Anne-Sophie BMC Med Inform Decis Mak Research Article BACKGROUND: Data collected in EHRs have been widely used to identifying specific conditions; however there is still a need for methods to define comorbidities and sources to identify comorbidities burden. We propose an approach to assess comorbidities burden for a specific disease using the literature and EHR data sources in the case of autoimmune diseases in celiac disease (CD). METHODS: We generated a restricted set of comorbidities using the literature (via the MeSH® co-occurrence file). We extracted the 15 most co-occurring autoimmune diseases of the CD. We used mappings of the comorbidities to EHR terminologies: ICD-10 (billing codes), ATC (drugs) and UMLS (clinical reports). Finally, we extracted the concepts from the different data sources. We evaluated our approach using the correlation between prevalence estimates in our cohort and co-occurrence ranking in the literature. RESULTS: We retrieved the comorbidities for 741 patients with CD. 18.1% of patients had at least one of the 15 studied autoimmune disorders. Overall, 79.3% of the mapped concepts were detected only in text, 5.3% only in ICD codes and/or drugs prescriptions, and 15.4% could be found in both sources. Prevalence in our cohort were correlated with literature (Spearman’s coefficient 0.789, p = 0.0005). The three most prevalent comorbidities were thyroiditis 12.6% (95% CI 10.1–14.9), type 1 diabetes 2.3% (95% CI 1.2–3.4) and dermatitis herpetiformis 2.0% (95% CI 1.0–3.0). CONCLUSION: We introduced a process that leveraged the MeSH terminology to identify relevant autoimmune comorbidities of the CD and several data sources from EHRs to phenotype a large population of CD patients. We achieved prevalence estimates comparable to the literature. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-017-0537-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-29 /pmc/articles/PMC5622531/ /pubmed/28962565 http://dx.doi.org/10.1186/s12911-017-0537-y Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Escudié, Jean-Baptiste Rance, Bastien Malamut, Georgia Khater, Sherine Burgun, Anita Cellier, Christophe Jannot, Anne-Sophie A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease |
title | A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease |
title_full | A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease |
title_fullStr | A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease |
title_full_unstemmed | A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease |
title_short | A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease |
title_sort | novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5622531/ https://www.ncbi.nlm.nih.gov/pubmed/28962565 http://dx.doi.org/10.1186/s12911-017-0537-y |
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