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@inproceedings{2240150,
author = {Anetta, Krištof},
address = {Brno},
booktitle = {Proceedings of the Sixteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2022},
editor = {Aleš Horák, Pavel Rychlý, Adam Rambousek},
keywords = {EHR; electronic health records; healthcare text; UMLS; ICD10; SNOMED CT; MedDRA; MeSH; NLP; natural language processing; Slavic languages; Polish; Czech; Slovak},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Brno},
isbn = {978-80-263-1752-4},
pages = {161-169},
publisher = {Tribun EU},
title = {Medical Knowledge Resources for Text-Mining of Health Records in Czech, Polish, and Slovak},
url = {https://nlp.fi.muni.cz/raslan/2022/paper14.pdf},
year = {2022}
}
TY - JOUR
ID - 2240150
AU - Anetta, Krištof
PY - 2022
TI - Medical Knowledge Resources for Text-Mining of Health Records in Czech, Polish, and Slovak
PB - Tribun EU
CY - Brno
SN - 9788026317524
KW - EHR
KW - electronic health records
KW - healthcare text
KW - UMLS
KW - ICD10
KW - SNOMED CT
KW - MedDRA
KW - MeSH
KW - NLP
KW - natural language processing
KW - Slavic languages
KW - Polish
KW - Czech
KW - Slovak
UR - https://nlp.fi.muni.cz/raslan/2022/paper14.pdf
N2 - Knowledge extraction from medical text in small languages like Czech, Polish or Slovak is challenging due to the insufficiency of languagespecific medical resources (pretrained models, ontologies, dictionaries). This paper is a survey of noteworthy options for researchers targeting these languages, divided into two sections. First, since the UMLS Metathesaurus for English is by far the most extensive and detailed medical knowledge resource in Western medicine, appreciable results can be achieved by machine-translating the mined text to English – therefore, the relevant English components of UMLS are introduced. Second come the languagespecific resources for each language, detailing the publishing institutions, current website locations, contents, and file formats. The contribution of this paper is in collecting and pre-screening widely disparate sources needed for successful medical knowledge extraction in Central European Slavic languages.
ER -
ANETTA, Krištof. Medical Knowledge Resources for Text-Mining of Health Records in Czech, Polish, and Slovak. In Aleš Horák, Pavel Rychlý, Adam Rambousek. \textit{Proceedings of the Sixteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2022}. Brno: Tribun EU, 2022, s.~161-169. ISBN~978-80-263-1752-4.
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