## 3 CER and Other NLP Methods for the Polish Language: Related Works  
For this article, a review of CER and other NLP methods for the Polish language was conducted, with the results presented in Table 1. Papers were selected through searches of the PubMed database and Google Scholar. The search queries included the terms \"machine learning\" and \"Polish\" or \"clinical entity recognition\" combined with \"Polish\". No restrictions were applied regarding the publication date. Before the topic of CER in the Polish language came into the picture, [PERSON] and [PERSON] [37] proposed automatic spelling correction for mammography reports. The goal of the suggested algorithm was to detect words and expressions that were written mistakenly--the types of errors that were detected were Polish diacritics, typical spelling errors, punctuation, and numbers and dates. To achieve that result, the authors created a domain dictionary that consists of words from medical reports from three Polish hospitals. This dictionary also contains the number of occurrences of specific words in the training set. The authors also prepared a second dataset, which included the Bigram model. Based on those and using minimal distance, the algorithm suggested how the word (or expression) should be written if it was detected as incorrect. The phrase was marked as incorrect if it could not be found in the domain dictionary. Even if this problem is not CER, it still addresses the issue of reports written as free speech, often with mistakes. Another attempt to use natural language processing in Polish medical reports can be found in [38]. The authors proposed an Information Extraction (IE) system based on real data from Polish healthcare institutions. In this case, custom ontology was created because none of the existing ontologies suited the task. This ontology was then used in rule-based IE tasks. Its purpose was to recognize attributes in texts correctly. Attributes were specified based on their occurrence in the test dataset. It is highlighted that creating such a system was challenging because of Polish grammar, ambiguous terms, and negations in texts. Moreover, the rule-based approach requires a lot of manual work and depends on the quality of the texts. For the tested dataset, the authors achieved high-precision results (close to 100%) but lower recall (for one of the attributes--87.37%). However, such a method can be applied only to a very narrow domain that needs to be well described by experts. In work [39], the authors present explainable machine learning for unsupervised models that divide medical records into groups. The used corpus consists of free-text notes written in Polish from about 100,000 clinical visits. This task aimed to group visits in clusters based on post-visit descriptions made by a doctor and then--as an application of achieved results--use information about similar visits to propose further treatment. The authors did not use any general model for the Polish language because of the narrow domain. Instead, they created embeddings for medical concepts with the GloVe algorithm. Those embeddings were used in vector representations of visits, so clustering using k-means and hierarchical clustering with [PERSON]'s method for merging clusters was possible. The authors chose to find similarities between visits, so they did not need to conduct classic named entity recognition tasks. However, they had to bother with Polish clinical records. In the CER field, in publication [40], the authors discuss if a general entity recognition system could be used in the medical domain for the Polish language. They conducted experiments using a custom dataset containing more than 23,000,000 Polish words from cardiology reports from the Medical University of Silesia in Katowice, Poland, called pl_ehr_cardio. The number of detected entities needed to be limited and mapped because of the use of generic tools such as PolDeepNer2, spaCy, or Spark NLP entity_recognizer_md. The final categories were person, organization, location, and miscellaneous.