Medical Knowledge Resources for Text-Mining of Health Records
in Czech, Polish, and Slovak

D 2022

Medical Knowledge Resources for Text-Mining of Health Records in Czech, Polish, and Slovak

ANETTA, Krištof

Základní údaje

Originální název

Medical Knowledge Resources for Text-Mining of Health Records in Czech, Polish, and Slovak

Autoři

ANETTA, Krištof (703 Slovensko, garant, domácí)

Vydání

Brno, Proceedings of the Sixteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2022, od s. 161-169, 9 s. 2022

Nakladatel

Tribun EU

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

Plný text Domovská stránka workshopu

Kód RIV

RIV/00216224:14330/22:00127483

Organizační jednotka

Fakulta informatiky

ISBN

978-80-263-1752-4

ISSN

Klíčová slova anglicky

EHR; electronic health records; healthcare text; UMLS; ICD10; SNOMED CT; MedDRA; MeSH; NLP; natural language processing; Slavic languages; Polish; Czech; Slovak

Změněno: 15. 5. 2024 09:26, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

EF19_073/0016943, projekt VaV

Název: Interní grantová agentura Masarykovy univerzity

MUNI/G/1763/2020, interní kód MU

Název: AIcope - AI support for Clinical Oncology and Patient Empowerment (Akronym: AIcope)

Investor: Masarykova univerzita, AIcope - AI support for Clinical Oncology and Patient Empowerment, INTERDISCIPLINARY - Mezioborové výzkumné projekty

MUNI/IGA/1326/2021, interní kód MU

Název: New Horizons of Electronic Health Record Analysis using Deep Learning (Akronym: Health Record Analysis using Deep Learning)

Investor: Masarykova univerzita, New Horizons of Electronic Health Record Analysis using Deep Learning

Citovat

ANETTA, Krištof. Medical Knowledge Resources for Text-Mining of Health Records in Czech, Polish, and Slovak. In Aleš Horák, Pavel Rychlý, Adam Rambousek. 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.

@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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