Active Learning Pipeline to Identify Candidate Terms for a CDSS Ontology

• Published in: Studies in health technology and informatics Vol. 316; p. 1338
• Main Authors: Jing, Xia, Goli, Rohan, Komatineni, Keerthana, Alluri, Shailesh, Hubig, Nina, Min, Hua, Gong, Yang, Sittig, Dean F, Biondich, Paul, Robinson, David, Nøhr, Christian, Faxvaag, Arild, Wright, Adam, Law, Timothy, Rennert, Lior, Gimbel, Ronald
• Format: Journal Article
• Language: English
• Published: Netherlands 22-08-2024
• Subjects: Biological Ontologies; Humans; Problem-Based Learning; Supervised Machine Learning; Terminology as Topic; Vocabulary, Controlled; deep learning; active learning; automatic keyphrase identification; natural language processing; Clinical decision support system ontology
• ISSN: 1879-8365

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.