ScholarGate
Msaidizi
Process / pipeline

Uchimbaji wa Madini wa Matini za Kimatibabu — Uchimbaji wa Taarifa za NLP za Kimatibabu

Uchimbaji wa madini wa matini za kimatibabu ni tawi maalumu la uchakataji wa lugha asilia linalochimba ukweli wa kimatibabu uliopangwa — utambuzi, dalili, dawa, matibabu, na misimbo ya ICD — kutoka kwa hati za afya zisizopangwa kama vile muhtasari wa kutoka hospitalini, madokezo ya maendeleo, na ripoti za radiolojia. Kwa kutumia miundo ya NLP ya kibayomediki kama vile BioBERT (Lee et al., 2020) na viwango vya changamoto za pamoja za i2b2/UTHealth (Stubbs & Uzuner, 2015), hubadilisha simulizi za matini huru za kimatibabu kuwa data zinazoweza kusomwa na mashine zinazofaa kwa usaidizi wa maamuzi ya kimatibabu na uchanganuzi wa afya.

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Vyanzo

  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

Jinsi ya kunukuu ukurasa huu

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

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Imerejelewa na

ScholarGateClinical Text Mining (Clinical Text Mining (Clinical NLP Information Extraction)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/text-mining/clinical-text-mining · Seti ya data: https://doi.org/10.5281/zenodo.20539026