## 3 CER and Other NLP Methods for the Polish Language: Related Works
Even though \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Publication** & **Task** & **Dataset** & **Approach** & **Results** & **Issues** & **Dataset Size** (Number of Records)** \\ \hline [PERSON]. [PERSON] and [PERSON] (2007) [37] & Spelling correction for mammography reports & Medical reports from 3 Polish hospitals & Domain dictionary with Bigram model corrections & Detected misspellings, suggested & Polish placerities, punctuation, and numbers/dates & 1400 \\ \hline [PERSON]. [PERSON] and [PERSON] (2009) [38] & Information Extraction (IE) system for healthcare texts & Real data from Polish healthcare institutions & Rule-based IE, custom ontology & High precision (close to 100\%), recall for one attribute: 87.37\% & Polish grammar, ambiguity, negations, domain-specific texts & 1473 \\ \hline [PERSON] et al. (2021) [39] & Grouping medical records for post-visit recommendations & Free-text notes from \(\sim\)100,000 clinical visits & GioVe embeddings, k-means, hierarchical clustering & Grouped similar visits for treatment suggestions & Narrow domain, free-text records & 100,000 \\ \hline [PERSON] et al. (2021) [40] & General entity recognition for medical texts & 23M+ Polish words from cardiology reports (pl_ehr_cardio) & PolDeepNer2, spaCy, Spark NLP & Exceededications for PolDeepNer2 & Limited categories, generic tools, narrow application in medical context & 50,000 \\ \hline [PERSON] et al. (2022) [41] & ICD-10 diagnosis prediction from the EHRs & 50,465 EHRs from cardiology ([PERSON]) & RoBERTa, BERT (Polish) & Accuracy: 68.79\% (admission), 78.64\% & Class imbalance, variation in diagnosis & 50,465 \\ \hline [PERSON] et al. (2023) [42] & Information Extraction from radiology reports & Radiology reports (University Clinical Centre, Gdansk) & Polish-roberta, distilroberta, Ingformer, HerBERT, mLuke & mLuke achieved best F1 score & \begin{tabular}{} \end{tabular} & 1200 \\ \hline [PERSON] et al. (2011) [43] & Creating annotated medical corpus & 460 hospital discharge reports (diabetes patients) & TEI P5 guidelines & \begin{tabular}{} \end{tabular} & Manual correction process ongoing & 460 \\ \hline [PERSON] et al. (2019) & Review of NER tools and corpora for Polish language & Multiple corpora (NkP, KPWr, CEN, PST) & \
\begin{tabular}{} \end{tabular} & Recognizes nested entities and 82 entity types & General and
\begin{tabular}{} \end{tabular} & Not given \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of key publications in Polish medical NLP. the tools were not designed for CER tasks, the authors claim that they met their expectations and even exceeded them for PolDeepNer2. However, the suggested categories are not tailored to the medical context, so the application of such an approach is very narrow. Other studies on cardiological records were conducted by the authors in [41]. They aimed to predict the ICD-10 diagnosis category based on Electronic Health Records. In their experiment, the dataset was obtained from the Asseco Medical Management Solutions (Asseco Poland S.A., Rzeszow, Poland) EHR system, including data from patients from the third Department of Cardiology, Leszek Giec Upper Silesian Medical Centre of the Medical University of Silesia in Katowice (GCM), Poland, and consists of 50,465 records. They were written as free text but with four sections--1. admission, 2. physical, 3. discharge, and 4. recommendation. The authors trained the models RoBERTa and BERT on Polish and multilingual data transformer language. It was necessary to handle class imbalance and variation in diagnosis. It was possible to obtain optimistic results. For models using admission data, accuracy in the best case equals 68.79%, and for models based on discharge data, the best accuracy was 78.64%. The authors concluded that systems able to attribute with ICD code automatically could benefit healthcare systems; however, its accuracy still needs improvement. Applying machine learning to the medical field in specific languages is limited because of the lack of adequately annotated datasets.