## 2 Related Works  
This section presents related works involving the adaptation of a pre-trained LLM to a specific domain to solve natural language processing tasks. In [7], a biomedical language representation model for mining large amounts of text was presented. BioBERT adapts the pre-trained linguistic model BERT to biomedical corpora, including PubMed abstracts and PMC full-text articles. This adaptation outperformed the original model and other state-of-the-art models with the same architecture in several biomedical text-mining tasks. BioBert improved named entity recognition (NER), biomedical relation extraction (REL), and biomedical text-mining tasks, showing that training BERT on biomedical corpora can help better understand a complex domain. BioBERT obtained a maximum F1 score of 86.51% in the relation extraction task, a value of 93.47% in the F-measure for the NER task, and a maximum lenient accuracy of 60% in the QA task. In [8], a model called ClinicalBERT was trained on patients' clinical notes, which contained laboratory values and medications, to process these notes and dynamically assign risk scores to predict whether a patient would be readmitted to the hospital within 30 days. The base model used was BERT, and the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, consisting of medical records from 58,976 hospital admissions of 38,597 patients between 2001 and 2012, was utilized. ClinicalBERT achieved an AUROC of 71.4%. Similarly, in the medical domain, the authors of [9] proposed adapting the Roberta model using texts from the biomedical and clinical literature in Spanish, aiming to improve NLP applications for Spanish in biomedicine. The authors of RobertaClinical confirmed that domain-specific pre-training is fundamental to obtaining better results in specific tasks such as NER and reported that they obtained an F-score of 88.21%. Similarly, [10] presented med-BERT, which recognized medical entities with an F1 score of 93% after being trained on clinical texts in English and Chinese. Models used to facilitate research in developing linguistic representations in the biomedical domain were presented in [11; 12]. These models were subjected to a five-task evaluation for biomedical language comprehension (BLUE and BLURB). In contrast, SciBERT [13] is a model adapted to the scientific publications of the Semantic Scholar repository. SciBERT was trained on 18% of articles corresponding to computer science and 82% corresponding to biomedicine, resulting in a hybrid model that was evaluated with tasks for information extraction. In a related development, models for health management have been proposed. For instance, in [14], the ConBERT model was used to forecast the International Classification of Disease-9 code by utilizing electronic health records and diagnoses in natural language text. It aims to standardize the automatic generation of medical reports. Additionally, in [15], a model utilizing the BERT-BiGRU-ATT architecture was introduced to extract connections between diseases, drugs, and drug effects from texts from online health communities in China. The model proved to be effective. The authors incorporated word embeddings into their model to account for contextual, grammatical, and semantic information within the biomedical field. The authors of [16] presented MarIA, which consists of a compendium of experiments with several Spanish language models, such as RoBERTa-base, RoBERTa-large [17], GPT2, and GPT2-large [18], pre-trained using a corpus of 135 billion words extracted from the Spanish Web Archive built by the National Library of Spain between the years 2009 and 2019. The performance of the models was evaluated using nine datasets for different NLP tasks in the general domain, as well as a question-answer set (SQAC) obtained from Spanish Wikipedia articles, encyclopedias, and Wikiewes. The best experimental results were an F1 score of 88.51% for the RoBERTa-base in the NER task and an F1 score of 98.56% for the RoBERTa-large model in the POS task.