The Medical Action Ontology : A tool for annotating and analyzing treatments and clinical management of human disease

Copyright © 2023. Published by Elsevier Inc..

BACKGROUND: Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions.

METHODS: MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology.

FINDINGS: MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases.

CONCLUSIONS: MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).

FUNDING: NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04.

Errataetall:

UpdateOf: medRxiv. 2023 Jul 13;:. - PMID 37503136

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:4

Enthalten in:

Med (New York, N.Y.) - 4(2023), 12 vom: 08. Dez., Seite 913-927.e3

Sprache:

Englisch

Beteiligte Personen:

Carmody, Leigh C [VerfasserIn]
Gargano, Michael A [VerfasserIn]
Toro, Sabrina [VerfasserIn]
Vasilevsky, Nicole A [VerfasserIn]
Adam, Margaret P [VerfasserIn]
Blau, Hannah [VerfasserIn]
Chan, Lauren E [VerfasserIn]
Gomez-Andres, David [VerfasserIn]
Horvath, Rita [VerfasserIn]
Kraus, Megan L [VerfasserIn]
Ladewig, Markus S [VerfasserIn]
Lewis-Smith, David [VerfasserIn]
Lochmüller, Hanns [VerfasserIn]
Matentzoglu, Nicolas A [VerfasserIn]
Munoz-Torres, Monica C [VerfasserIn]
Schuetz, Catharina [VerfasserIn]
Seitz, Berthold [VerfasserIn]
Similuk, Morgan N [VerfasserIn]
Sparks, Teresa N [VerfasserIn]
Strauss, Timmy [VerfasserIn]
Swietlik, Emilia M [VerfasserIn]
Thompson, Rachel [VerfasserIn]
Zhang, Xingmin Aaron [VerfasserIn]
Mungall, Christopher J [VerfasserIn]
Haendel, Melissa A [VerfasserIn]
Robinson, Peter N [VerfasserIn]

Links:

Volltext

Themen:

Clinical management
Computational decision support
Foundational research
Journal Article
MAxO
Medical action ontology
Ontology
Surgical procedure
Treatment

Anmerkungen:

Date Completed 16.12.2023

Date Revised 14.02.2024

published: Print-Electronic

UpdateOf: medRxiv. 2023 Jul 13;:. - PMID 37503136

Citation Status MEDLINE

doi:

10.1016/j.medj.2023.10.003

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM364550791