Fine-Tuning an Existing Large Language Model with Knowledge from the Medical Expert System Hepaxpert...35th Medical Informatics Europe Conference (MIE 2025), May 19-21, 2025, Glasgow, Scotland.
The analysis and individual interpretation of hepatitis serology test results is a complex task in laboratory medicine, requiring either experienced physicians or specialized expert systems. This study explores fine-tuning a large language model (LLM) for hepatitis serology interpretation using a si...
| Publicado en: | Studies in Health Technology & Informatics Vol. 327; pp. 143 - 148 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185459131&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185459131 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 327 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 185459131 185459131 185459131 10.3233/SHTI250290 185459131 ppf: 143 ppct: 5 formats: tig: atl: Fine-Tuning an Existing Large Language Model with Knowledge from the Medical Expert System Hepaxpert...35th Medical Informatics Europe Conference (MIE 2025), May 19-21, 2025, Glasgow, Scotland. aug: au: KAINZ, Jakob SEISL, Philipp GROB, Moritz HAUPTFELD, Leonhard WAHRINGER, Jonas RAPPELSBERGER, Andrea ADLASSNIG, Klaus-Peter affil: Medexter Healthcare, Borschkegasse 7/5, 1090 Vienna, Austria. sug: subj: Natural Language Processing Hepatitis Diagnosis Serology Image Processing, Computer Assisted Decision Support Systems, Clinical Human Congresses and Conferences Scotland Scotland Algorithms ab: The analysis and individual interpretation of hepatitis serology test results is a complex task in laboratory medicine, requiring either experienced physicians or specialized expert systems. This study explores fine-tuning a large language model (LLM) for hepatitis serology interpretation using a single graphics processing unit (GPU). A custom dataset based on the Hepaxpert expert system was used to train the LLM. Fine-tuning was performed on an Nvidia RTX 6000 Ada GPU via torchtune. The fine-tuned LLM showed significant performance improvements over the base model when compared to Hepaxpert using the METEOR algorithm. The findings highlight the potential of LLMs in enhancing medical expert systems as well as the significance of domain-specific fine-tuning. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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