Evaluating the portability of automatic note classification methods with the LOINC Document Ontology

Presentation Time: 10:30 AM - 10:45 AM

Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Natural Language Processing, Reproducibility
Primary Track: Applications

To utilize clinical notes for research studies, it is necessary to identify the most relevant notes. Mapping to the LOINC Document Ontology makes this process easier by reducing the variability of note types. We used a BERT model to automatically identify LOINC DO entities in VA note titles. Future work will involve the use of additional note metadata and contents to improve note classification.

Speaker(s):
Annie Bowles, MS Biomedical Informatics
VHA Salt Lake City Health Care System

Author(s):
Patrick Alba, MS - United States Department of Veterans Affairs; Jianlin Shi, MS, MD - The Division of Epidemiology, School of Medicine, University of Utah; VA Salt Lake City Healthcare System; Qiwei Gan; Scott DuVall, PhD - VA Salt Lake City Health Care System; Elizabeth Hanchrow, RN, MSN - Veterans Affairs and WIVR;