Transferability of General Polish NER to Electronic Health Records

ANETTA, Krištof and Mahmut ARSLAN. Transferability of General Polish NER to Electronic Health Records. In Horák, Rychlý, Rambousek. Recent Advances in Slavonic Natural Language Processing (RASLAN 2021). Brno: Tribun EU, 2021, p. 151-159. ISBN 978-80-263-1670-1.

Basic information

• Original name: Transferability of General Polish NER to Electronic Health Records
• Authors: ANETTA, Krištof (203 Czech Republic, guarantor, belonging to the institution) and Mahmut ARSLAN (792 Turkey).
• Edition: Brno, Recent Advances in Slavonic Natural Language Processing (RASLAN 2021), p. 151-159, 9 pp. 2021.
• 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: Domovská stránka workshopu Full text PDF
• RIV identification code: RIV/00216224:14330/21:00123253
• Organization unit: Faculty of Informatics
• ISBN: 978-80-263-1670-1
• ISSN: 2336-4289
• Keywords in English: EHR; Electronic health records; Healthcare texts; NER; Named entity recognition; NLP; Natural language processing; Slavic languages; Polish; PolDeepNer2; spaCy; Spark NLP

Abstract

This paper investigates the transferability of general Polish named entity recognition tools to the analysis of Polish health records. The tools, namely PolDeepNer2, spaCy’s pl_core_news_lg pipeline and Spark NLP’s entity_recognizer_md pipeline for Polish, were run on the pl_ehr_cardio corpus and their results were analyzed, paying special atten- tion to their performance when processing these highly specific texts and to the applicability of the results in the healthcare domain. Even though the precision of PolDeepNer2 proved to be superior to both spaCy and Spark NLP, the paper concludes that without additional training, general named entity recognition tools for Polish have very limited use in the medi- cal analysis of electronic health records. However, they could be helpful in partial tasks ranging from de-identification to entity disambiguation and discovery of mistyped entities or candidate entities that are not present in medical dictionaries.

Links

• LM2018101, research and development project: Name: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy (Acronym: LINDAT/CLARIAH-CZ)

Investor: Ministry of Education, Youth and Sports of the CR

• MUNI/IGA/1505/2020, interní kód MU: Name: Electronic Health Record Analysis using Deep Learning (Acronym: Health Record Analysis with Deep Learning)

Investor: Masaryk University