Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology

George Despotou; Ioannis Korkontzelos; Theodoros Arvanitis

doi:10.3233/shti220267

Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology

George Despotou, Ioannis Korkontzelos, Theodoros Arvanitis

Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Background: CIGs languages consist of approach specific concepts. More widely used concepts, such as those in UMLS are not typically used.

Objective: An evaluation of UMLS concept sufficiency for CIG definition.

Method: A popular guideline is mapped to UMLS concepts with NLP. Results are reviewed to evaluate gaps, and appropriateness.

Results: A significant number of the guideline text mapped to UMLS concepts.

Conclusions: The approach has shown promise and highlighted further challenges.

Original language	English
Title of host publication	MEDINFO 2021
Subtitle of host publication	One World, One Health – Global Partnership for Digital Innovation
Editors	Paula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing
Publisher	IOS Press
Pages	1060-1061
Number of pages	2
ISBN (Electronic)	9781643682655
ISBN (Print)	9781643682648
DOIs	https://doi.org/10.3233/shti220267
Publication status	Published - 6 Jun 2022
Event	MEDINFO 2021: One World, One Health: Global Partnership for Digital Innovation - Virtual Duration: 2 Oct 2021 → 4 Oct 2021

Publication series

Name	Studies in Health Technology and Informatics
Volume	290
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	MEDINFO 2021
Period	2/10/21 → 4/10/21

Keywords

natural language processing
knowledge representation
practice guideline
computer interpretable guidelines

Access to Document

10.3233/shti220267Licence: Creative Commons: Attribution-NonCommercial (CC BY-NC)

DespotouG2022BottomFinal published version, 161 KBLicence: Creative Commons: Attribution-NonCommercial (CC BY-NC)

Cite this

Despotou, G., Korkontzelos, I., & Arvanitis, T. (2022). Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology. In P. Otero, P. Scott, S. Z. Martin, & E. Huesing (Eds.), MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation (pp. 1060-1061). (Studies in Health Technology and Informatics; Vol. 290). IOS Press. https://doi.org/10.3233/shti220267

Despotou, George ; Korkontzelos, Ioannis ; Arvanitis, Theodoros. / Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology. MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. editor / Paula Otero ; Philip Scott ; Susan Z. Martin ; Elaine Huesing. IOS Press, 2022. pp. 1060-1061 (Studies in Health Technology and Informatics).

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title = "Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology",

abstract = "Background: CIGs languages consist of approach specific concepts. More widely used concepts, such as those in UMLS are not typically used. Objective: An evaluation of UMLS concept sufficiency for CIG definition. Method: A popular guideline is mapped to UMLS concepts with NLP. Results are reviewed to evaluate gaps, and appropriateness. Results: A significant number of the guideline text mapped to UMLS concepts. Conclusions: The approach has shown promise and highlighted further challenges.",

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author = "George Despotou and Ioannis Korkontzelos and Theodoros Arvanitis",

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Despotou, G, Korkontzelos, I & Arvanitis, T 2022, Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology. in P Otero, P Scott, SZ Martin & E Huesing (eds), MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. Studies in Health Technology and Informatics, vol. 290, IOS Press, pp. 1060-1061, MEDINFO 2021, 2/10/21. https://doi.org/10.3233/shti220267

Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology. / Despotou, George; Korkontzelos, Ioannis; Arvanitis, Theodoros.
MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. ed. / Paula Otero; Philip Scott; Susan Z. Martin; Elaine Huesing. IOS Press, 2022. p. 1060-1061 (Studies in Health Technology and Informatics; Vol. 290).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology

AU - Despotou, George

AU - Korkontzelos, Ioannis

AU - Arvanitis, Theodoros

PY - 2022/6/6

Y1 - 2022/6/6

N2 - Background: CIGs languages consist of approach specific concepts. More widely used concepts, such as those in UMLS are not typically used. Objective: An evaluation of UMLS concept sufficiency for CIG definition. Method: A popular guideline is mapped to UMLS concepts with NLP. Results are reviewed to evaluate gaps, and appropriateness. Results: A significant number of the guideline text mapped to UMLS concepts. Conclusions: The approach has shown promise and highlighted further challenges.

AB - Background: CIGs languages consist of approach specific concepts. More widely used concepts, such as those in UMLS are not typically used. Objective: An evaluation of UMLS concept sufficiency for CIG definition. Method: A popular guideline is mapped to UMLS concepts with NLP. Results are reviewed to evaluate gaps, and appropriateness. Results: A significant number of the guideline text mapped to UMLS concepts. Conclusions: The approach has shown promise and highlighted further challenges.

KW - natural language processing

KW - knowledge representation

KW - practice guideline

KW - computer interpretable guidelines

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DO - 10.3233/shti220267

M3 - Conference contribution

SN - 9781643682648

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A2 - Otero, Paula

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Despotou G, Korkontzelos I, Arvanitis T. Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology. In Otero P, Scott P, Martin SZ, Huesing E, editors, MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press. 2022. p. 1060-1061. (Studies in Health Technology and Informatics). doi: 10.3233/shti220267

Bottom-Up Natural Language Processing Based Evaluation of the Fitness of UMLS as a Semantic Source for a Computer Interpretable Guidelines Ontology

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