Use of SNOMED CT in Large Language Models: Scoping Review.

Background: Large language models (LLMs) have substantially advanced natural language processing (NLP) capabilities but often struggle with knowledge-driven tasks in specialized domains such as biomedicine. Integrating biomedical knowledge sources such as SNOMED CT into LLMs may enhance their performance on biomedical tasks. However, the methodologies and effectiveness of incorporating SNOMED CT into LLMs have not been systematically reviewed.

Harmonization of Data Across Cohorts Using Standard Terminologies.

Korean National Institute of Health initiated data harmonization across cohorts with the aim to ensure semantic interoperability of data and to create a common database of standardized data elements for future collaborative research. With this aim, we reviewed code books of cohorts and identified common data items and values which can be combined for data analyses. We then mapped data items and values to standard health terminologies such as SNOMED CT.

Cohort Identification from Free-Text Clinical Notes Using SNOMED CT's Hierarchical Semantic Relations.

In this paper, a new cohort identification system that exploits the semantic hierarchy of SNOMED CT is proposed to overcome the limitations of supervised machine learning-based approaches. Eligibility criteria descriptions and free-text clinical notes from the 2018 National NLP Clinical Challenge (n2c2) were processed to map to relevant SNOMED CT concepts and to measure semantic similarity between the eligibility criteria and patients. The eligibility of a patient was determined if the patient had a similarity score higher than a threshold cut-off value.

A vector-based semantic relatedness measure using multiple relations within SNOMED CT and UMLS.

Objective: To propose a new vector-based relatedness metric that derives word vectors from the intrinsic structure of biomedical ontologies, without consulting external resources such as large-scale biomedical corpora.

Materials And Methods: SNOMED CT on the mapping layer of UMLS was used as a testbed ontology. Vectors were created for every concept at the end of all semantic relations-attribute-value relations and descendants as well as is_a relation-of the defining concept.

The use of SNOMED CT, 2013-2020: a literature review.

Objective: This article reviews recent literature on the use of SNOMED CT as an extension of Lee et al's 2014 review on the same topic. The Lee et al's article covered literature published from 2001-2012, and the scope of this review was 2013-2020.

Materials And Methods: In line with Lee et al's methods, we searched the PubMed and Embase databases and identified 1002 articles for review, including studies from January 2013 to September 2020.

Helminthiases in North Korea: a neglected public health challenge.

Helminthiasis is a neglected public health challenge threatening North Koreans. However, there is no precise estimate on the burden of helminthiasis in the region. This study therefore reviewed existing literature with the aim of analyzing the current state of helminthiasis among North Koreans, as well as highlighting the difference in the prevalence of helminthiasis between North Korean residents and refugees.

Clinical Markers Associated with Metabolic Syndrome Among Military Aviators.

Background: Due to the recent increase of metabolic syndrome (MetS) in the Korean population, this study was performed to investigate the prevalence of MetS among Republic of Korea (ROK) Air Force military aviators and its relationship with clinical markers.

Methods: A cross-sectional study was performed among 911 aviators who filled out the lifestyle questionnaire and underwent medical examinations at the ROK Air Force Aerospace Medical Center. Clinical markers of aviators with MetS were investigated and odds ratios were calculated.