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

Basic information

Original name

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

Authors

ANETTA, Krištof (703 Slovakia, guarantor, belonging to the institution)

Edition

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

Publisher

Tribun EU

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

Plný text Domovská stránka workshopu

RIV identification code

RIV/00216224:14330/22:00127483

Organization unit

Faculty of Informatics

ISBN

978-80-263-1752-4

ISSN

Keywords in English

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.

Abstract

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.

Links

EF19_073/0016943, research and development project

Name: Interní grantová agentura Masarykovy univerzity

MUNI/G/1763/2020, interní kód MU

Name: AIcope - AI support for Clinical Oncology and Patient Empowerment (Acronym: AIcope)

Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects

MUNI/IGA/1326/2021, interní kód MU

Name: New Horizons of Electronic Health Record Analysis using Deep Learning (Acronym: Health Record Analysis using Deep Learning)

Investor: Masaryk University

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, p. 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, p.~161-169. ISBN~978-80-263-1752-4.

Detailed Information on Publication Record