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@inproceedings{1809764,
author = {Anetta, Krištof and Arslan, Mahmut},
address = {Brno},
booktitle = {Recent Advances in Slavonic Natural Language Processing (RASLAN 2021)},
editor = {Horák, Rychlý, Rambousek},
keywords = {EHR; Electronic health records; Healthcare texts; NER; Named entity recognition; NLP; Natural language processing; Slavic languages; Polish; PolDeepNer2; spaCy; Spark NLP},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Brno},
isbn = {978-80-263-1670-1},
pages = {151-159},
publisher = {Tribun EU},
title = {Transferability of General Polish NER to Electronic Health Records},
url = {https://raslan2021.nlp-consulting.net/},
year = {2021}
}
TY - JOUR
ID - 1809764
AU - Anetta, Krištof - Arslan, Mahmut
PY - 2021
TI - Transferability of General Polish NER to Electronic Health Records
PB - Tribun EU
CY - Brno
SN - 9788026316701
KW - EHR
KW - Electronic health records
KW - Healthcare texts
KW - NER
KW - Named entity recognition
KW - NLP
KW - Natural language processing
KW - Slavic languages
KW - Polish
KW - PolDeepNer2
KW - spaCy
KW - Spark NLP
UR - https://raslan2021.nlp-consulting.net/
N2 - 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.
ER -
ANETTA, Krištof a Mahmut ARSLAN. Transferability of General Polish NER to Electronic Health Records. In Horák, Rychlý, Rambousek. \textit{Recent Advances in Slavonic Natural Language Processing (RASLAN 2021)}. Brno: Tribun EU, 2021, s.~151-159. ISBN~978-80-263-1670-1.
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