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Kugic, A; Pfeifer, B; Schulz, S; Kreuzthaler, M.
Embedding-based terminology expansion via secondary use of large clinical real-world datasets.
J Biomed Inform. 2023; 147:104497 Doi: 10.1016/j.jbi.2023.104497
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Leading authors Med Uni Graz: Kreuzthaler Markus Eduard; Kugic Amila
Co-authors Med Uni Graz: Pfeifer Bastian; Schulz Stefan
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Abstract:: A log-likelihood based co-occurrence analysis of ∼1.9 million de-identified ICD-10 codes and related short textual problem list entries generated possible term candidates at a significance level of p<0.01. These top 10 term candidates, consisting of 1 to 5-grams, were used as seed terms for an embedding based nearest neighbor approach to fetch additional synonyms, hypernyms and hyponyms in the respective n-gram embedding spaces by leveraging two different language models. This was done to analyze the lexicality of the resulting term candidates and to compare the term classifications of both models. We found no difference in system performance during the processing of lexical and non-lexical content, i.e. abbreviations, acronyms, etc. Additionally, an application-oriented analysis of the SapBERT (Self-Alignment Pretraining for Biomedical Entity Representations) language model indicates suitable performance for the extraction of all term classifications such as synonyms, hypernyms, and hyponyms.
Find related publications in this database (using NLM MeSH Indexing): Natural Language Processing - administration & dosage; Likelihood Functions - administration & dosage; Language - administration & dosage; Cluster Analysis - administration & dosage
Find related publications in this database (Keywords): Natural language processing; Big data; Electronic health records; International classification of diseases; Machine learning