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Klinisk tekstmining — Klinisk NLP-informationsudtræk

Klinisk tekstmining er en specialiseret gren af naturlig sprogbehandling (NLP), der udtrækker strukturerede kliniske fakta – diagnoser, symptomer, medicin, behandlinger og ICD-koder – fra ustrukturerede sundhedsdokumenter som udskrivningsresuméer, journalnotater og radiologiske rapporter. Med udgangspunkt i biomedicinske NLP-modeller som BioBERT (Lee et al., 2020) og i2b2/UTHealth shared-task benchmarks (Stubbs & Uzuner, 2015) omdanner den kliniske narrativer i fritekst til maskinlæsbare data, der er egnede til klinisk beslutningsstøtte og sundhedsanalyser.

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  1. Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240. DOI: 10.1093/bioinformatics/btz682
  2. Stubbs, A. & Uzuner, Ö. (2015). Annotating risk factors for heart disease in clinical narratives for the 2014 i2b2/UTHealth shared task. Journal of the American Medical Informatics Association, 22(e1), e30–e39. link

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ScholarGate. (2026, June 1). Clinical Text Mining (Clinical NLP Information Extraction). ScholarGate. https://scholargate.app/da/text-mining/clinical-text-mining

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ScholarGateClinical Text Mining (Clinical Text Mining (Clinical NLP Information Extraction)). Hentet 2026-06-15 fra https://scholargate.app/da/text-mining/clinical-text-mining · Datasæt: https://doi.org/10.5281/zenodo.20539026