Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Kugic, A; Kreuzthaler, M; Schulz, S.
Clinical Acronym Disambiguation via ChatGPT and BING.
Stud Health Technol Inform. 2023; 309:78-82
Doi: 10.3233/SHTI230743
PubMed
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- Führende Autor*innen der Med Uni Graz
- Kugic Amila
- Co-Autor*innen der Med Uni Graz
- Kreuzthaler Markus Eduard
- Schulz Stefan
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- Abstract:
- Clinical texts are written with acronyms, abbreviations and medical jargon expressions to save time. This hinders full comprehension not just for medical experts but also laypeople. This paper attempts to disambiguate acronyms with their given context by comparing a web mining approach via the search engine BING and a conversational agent approach using ChatGPT with the aim to see, if these methods can supply a viable resolution for the input acronym. Both approaches are automated via application programming interfaces. Possible term candidates are extracted using natural language processing-oriented functionality. The conversational agent approach surpasses the baseline for web mining without plausibility thresholds in precision, recall and F1-measure, while scoring similarly only in precision for high threshold values.
- Find related publications in this database (using NLM MeSH Indexing)
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