Faculty, Staff and Student Publications

Language

English

Publication Date

8-22-2024

Journal

Studies in Health Technology and Informatics

DOI

10.3233/SHTI240660

PMID

39176629

PMCID

PMC12149559

PubMedCentral® Posted Date

6-10-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Ontology is essential for achieving health information and information technology application interoperability in the biomedical fields and beyond. Traditionally, ontology construction is carried out manually by human domain experts (HDE). Here, we explore an active learning approach to automatically identify candidate terms from publications, with manual verification later as a part of a deep learning model training and learning process. We introduce the overall architecture of the active learning pipeline and present some preliminary results. This work is a critical and complementary component in addition to manually building the ontology, especially during the long-term maintenance stage.

Keywords

Humans, Biological Ontologies, Terminology as Topic, Problem-Based Learning, Supervised Machine Learning, Vocabulary, Controlled, Clinical decision support system ontology, active learning, deep learning, automatic keyphrase identification, natural language processing

Published Open-Access

yes

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