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An updated, computable MEDication-Indication resource for biomedical research

The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for modern EHRs. Indications for prescribable medications...

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Autores principales: Zheng, Neil S., Kerchberger, V. Eric, Borza, Victor A., Eken, H. Nur, Smith, Joshua C., Wei, Wei-Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460636/
https://www.ncbi.nlm.nih.gov/pubmed/34556781
http://dx.doi.org/10.1038/s41598-021-98579-4
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author Zheng, Neil S.
Kerchberger, V. Eric
Borza, Victor A.
Eken, H. Nur
Smith, Joshua C.
Wei, Wei-Qi
author_facet Zheng, Neil S.
Kerchberger, V. Eric
Borza, Victor A.
Eken, H. Nur
Smith, Joshua C.
Wei, Wei-Qi
author_sort Zheng, Neil S.
collection PubMed
description The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for modern EHRs. Indications for prescribable medications were extracted using natural language processing and ontology relationships from six publicly available resources: RxNorm, Side Effect Resource 4.1, Mayo Clinic, WebMD, MedlinePlus, and Wikipedia. We compared the estimated precision and recall between the previous MEDI (MEDI-1) and the updated version (MEDI-2) with manual review. MEDI-2 contains 3031 medications and 186,064 indications. The MEDI-2 high precision subset (HPS) includes indications found within RxNorm or at least three other resources. MEDI-2 and MEDI-2 HPS contain 13% more medications and over triple the indications compared to MEDI-1 and MEDI-1 HPS, respectively. Manual review showed MEDI-2 achieves the same precision (0.60) with better recall (0.89 vs. 0.79) compared to MEDI-1. Likewise, MEDI-2 HPS had the same precision (0.92) and improved recall (0.65 vs. 0.55) than MEDI-1 HPS. The combination of MEDI-1 and MEDI-2 achieved a recall of 0.95. In updating MEDI, we present a more comprehensive medication-indication knowledgebase that can continue to facilitate applications and research with EHRs.
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spelling pubmed-84606362021-09-27 An updated, computable MEDication-Indication resource for biomedical research Zheng, Neil S. Kerchberger, V. Eric Borza, Victor A. Eken, H. Nur Smith, Joshua C. Wei, Wei-Qi Sci Rep Article The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for modern EHRs. Indications for prescribable medications were extracted using natural language processing and ontology relationships from six publicly available resources: RxNorm, Side Effect Resource 4.1, Mayo Clinic, WebMD, MedlinePlus, and Wikipedia. We compared the estimated precision and recall between the previous MEDI (MEDI-1) and the updated version (MEDI-2) with manual review. MEDI-2 contains 3031 medications and 186,064 indications. The MEDI-2 high precision subset (HPS) includes indications found within RxNorm or at least three other resources. MEDI-2 and MEDI-2 HPS contain 13% more medications and over triple the indications compared to MEDI-1 and MEDI-1 HPS, respectively. Manual review showed MEDI-2 achieves the same precision (0.60) with better recall (0.89 vs. 0.79) compared to MEDI-1. Likewise, MEDI-2 HPS had the same precision (0.92) and improved recall (0.65 vs. 0.55) than MEDI-1 HPS. The combination of MEDI-1 and MEDI-2 achieved a recall of 0.95. In updating MEDI, we present a more comprehensive medication-indication knowledgebase that can continue to facilitate applications and research with EHRs. Nature Publishing Group UK 2021-09-23 /pmc/articles/PMC8460636/ /pubmed/34556781 http://dx.doi.org/10.1038/s41598-021-98579-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zheng, Neil S.
Kerchberger, V. Eric
Borza, Victor A.
Eken, H. Nur
Smith, Joshua C.
Wei, Wei-Qi
An updated, computable MEDication-Indication resource for biomedical research
title An updated, computable MEDication-Indication resource for biomedical research
title_full An updated, computable MEDication-Indication resource for biomedical research
title_fullStr An updated, computable MEDication-Indication resource for biomedical research
title_full_unstemmed An updated, computable MEDication-Indication resource for biomedical research
title_short An updated, computable MEDication-Indication resource for biomedical research
title_sort updated, computable medication-indication resource for biomedical research
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460636/
https://www.ncbi.nlm.nih.gov/pubmed/34556781
http://dx.doi.org/10.1038/s41598-021-98579-4
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