Publication details

Transferability of General Polish NER to Electronic Health Records


ANETTA Krištof ARSLAN Mahmut

Year of publication 2021
Type Article in Proceedings
Conference Recent Advances in Slavonic Natural Language Processing (RASLAN 2021)
MU Faculty or unit

Faculty of Informatics

Keywords EHR; Electronic health records; Healthcare texts; NER; Named entity recognition; NLP; Natural language processing; Slavic languages; Polish; PolDeepNer2; spaCy; Spark NLP
Description 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.
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