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Linking multiple disease-related resources through UMLS

Authors:

Aurélie Névéol,

Jiao Li,

Zhiyong LuAuthors Info & Claims

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

Pages 767 - 772

https://doi.org/10.1145/2110363.2110455

Published: 28 January 2012 Publication History

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Abstract

A recent usage log analysis showed that disease information is frequently sought by PubMed users. Besides PubMed, many other resources provide valuable information on thousands of diseases for scientific professionals and health consumers. However, the lack of explicit links between resources limits the access to comprehensive information for a given disease. The objective of this work is to integrate a variety of disease-related resources in the public domain in order to enable integrated access to multiple disease resources. We applied automated methods for recognizing and mapping disease mentions in free text to disease concepts in UMLS. A total of 467 Gene Reviews and 1,581 A.D.A.M. disease records were mapped to UMLS concepts. These mappings complement manually curated associations and enable the automatic creation of relevant links between documents. With minimal human intervention, disease-related resources were mapped to UMLS and linked together, which is critical for providing integrated access to online disease information.

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Cited By

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Wang LShan MZhou TRyu K(2023)Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical DataApplied Sciences10.3390/app13201115113:20(11151)Online publication date: 10-Oct-2023
https://doi.org/10.3390/app132011151
Jing X(2020)UMLS at 30 years: How it is used and published (Preprint)JMIR Medical Informatics10.2196/20675Online publication date: 25-May-2020
https://doi.org/10.2196/20675
Doğan RLeaman RLu Z(2018)NCBI disease corpusJournal of Biomedical Informatics10.5555/2772763.277280047:C(1-10)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.5555/2772763.2772800
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Linking multiple disease-related resources through UMLS
1. Applied computing
  1. Life and medical sciences

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Published In

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

January 2012

914 pages

ISBN:9781450307819

DOI:10.1145/2110363

General Chairs:
Gang Luo
IBM Research, USA
,
Jiming Liu
Hong Kong Baptist University
,
Program Chair:
Christopher C. Yang
Drexel University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 28 January 2012

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IHI '12: ACM International Health Informatics Symposium

January 28 - 30, 2012

Florida, Miami, USA

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Wang LShan MZhou TRyu K(2023)Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical DataApplied Sciences10.3390/app13201115113:20(11151)Online publication date: 10-Oct-2023
https://doi.org/10.3390/app132011151
Jing X(2020)UMLS at 30 years: How it is used and published (Preprint)JMIR Medical Informatics10.2196/20675Online publication date: 25-May-2020
https://doi.org/10.2196/20675
Doğan RLeaman RLu Z(2018)NCBI disease corpusJournal of Biomedical Informatics10.5555/2772763.277280047:C(1-10)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.5555/2772763.2772800
Doğan RLeaman RLu Z(2018)Special ReportJournal of Biomedical Informatics10.5555/2598938.259912747(1-10)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.5555/2598938.2599127
Xu KZhou ZGong THao TLiu W(2018)SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fieldsBMC Medical Informatics and Decision Making10.1186/s12911-018-0690-y18:S5Online publication date: 7-Dec-2018
https://doi.org/10.1186/s12911-018-0690-y
Dhayne HKilany RHaque RTaher Y(2018)SeDIE: A Semantic-Driven Engine for Integration of Healthcare Data2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2018.8621243(617-622)Online publication date: Dec-2018
https://doi.org/10.1109/BIBM.2018.8621243
Krallinger MRabal OLourenço AOyarzabal JValencia A(2017)Information Retrieval and Text Mining Technologies for ChemistryChemical Reviews10.1021/acs.chemrev.6b00851117:12(7673-7761)Online publication date: 5-May-2017
https://doi.org/10.1021/acs.chemrev.6b00851
Doğan RLeaman RLu Z(2014)NCBI disease corpus: A resource for disease name recognition and concept normalizationJournal of Biomedical Informatics10.1016/j.jbi.2013.12.00647(1-10)Online publication date: Feb-2014
https://doi.org/10.1016/j.jbi.2013.12.006
Cheng LWang GLi JZhang TXu PWang Y(2013)SIDD: A Semantically Integrated Database towards a Global View of Human DiseasePLoS ONE10.1371/journal.pone.00755048:10(e75504)Online publication date: 11-Oct-2013
https://doi.org/10.1371/journal.pone.0075504
Leaman RIslamaj Doğan RLu Z(2013)DNorm: disease name normalization with pairwise learning to rankBioinformatics10.1093/bioinformatics/btt47429:22(2909-2917)Online publication date: 21-Aug-2013
https://doi.org/10.1093/bioinformatics/btt474
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PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking

Automatic integration of drug indications from multiple health resources

Semantics based approach for analyzing disease-target associations

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