Infertility treatment begins with an interview, during which all medical information related to the patient’s history and diagnostic test results are collected. Next, a physician may determine the most appropriate direction for further treatment. We hypothesize that a task-specific deep learning (DL) model could support clinical decisions. However, there is a lack of Polish medical language corpus and language-specific medical DL models. Thus, we tested three general-use Natural Language Processing (NLP) models trained on Polish word collections: (i) bert-base-polish-cased-v1; (ii) herbert-base-cased; (iii) polish-roberta-base-v2. We evaluated the efficacy of the models using 80 Electronic Health Records (EHRs) with information obtained during the medical interview to predict the future stages of infertility treatment. The best Polish medical text classification model was the bert-base-polish-cased-v1 trained on pre-processed data. The lack of a Polish medical corpus decreased the results’ interpretability. Nevertheless, we believe that thanks to the developed model, it will be possible to estimate the pregnancy and decisions on the choice of treatment method more precisely.