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.
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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
Original language English
Type of outcome Proceedings paper
Field of Study 10200 1.2 Computer and information sciences
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW 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 2336-4289
Keywords in English EHR; electronic health records; healthcare text; UMLS; ICD10; SNOMED CT; MedDRA; MeSH; NLP; natural language processing; Slavic languages; Polish; Czech; Slovak
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 6/4/2023 10:02.
Abstract
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 projectName: Interní grantová agentura Masarykovy univerzity
MUNI/G/1763/2020, interní kód MUName: AIcope - AI support for Clinical Oncology and Patient Empowerment (Acronym: AIcope)
Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects
MUNI/IGA/1326/2021, interní kód MUName: New Horizons of Electronic Health Record Analysis using Deep Learning (Acronym: Health Record Analysis using Deep Learning)
Investor: Masaryk University
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