## 1 Introduction
One of the advantages of DL is its ability to minimize the tasks of preprocessing and feature engineering [10; 11]. This is particularly valuable in the context of clinical text, which exhibits high variability, a prevalence of grammatical errors, and a multitude of acronyms and other challenges. Therefore, leveraging DL models becomes a practical and effective solution. Instead of using a Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN), transformer models can efficiently process variable-length sequences of data,allowing for the capture of long-range dependency relationships in text and other types of sequential data. A transformer-based model is a deep network that seeks to numerically represent each word according to its context based on self-attention [12]. To obtain an adequate numerical representation of each word, the models are trained in an initial phase to solve masked language model (MLM) tasks on a large number of texts [13]. These models have been widely used in NLP tasks, including text generation, machine translation, text summarization, etc. In particular, transformer-based models have had a significant impact in biomedical NER, where they are used to identify relevant entities in the healthcare domain, such as genes, proteins, diseases, and treatments, which can have a major impact on medical research and medical decision-making [14]. A large number of parameters that need to be adjusted in transformer-based models involve using systems with high hardware requirements over several days, which are time-consuming, leading to high costs when training these models [15]. The use of transformers has promoted the development of linguistic models, in addition to the creation of platforms, such as Hugging Face, that allow for hosting and sharing these models, which has facilitated a rapid adaptation to new domains on specific tasks [16]. Therefore, the generation of new models for the particularities of the Spanish clinical domain, in most cases, has focused on adding and adjusting one or more layers on existing models, a process known as fine-tuning [17]. In the end, we will have as a result models that are adjusted to specific tasks, such as NER, in which the learning of the original models is transferred. Other studies have explored the use of general-purpose large language models (LLMs) such as GPT, LLaMA [18], Gemini [19], and Claude, applying prompt engineering techniques to perform NER tasks [20]. Nevertheless, several works have shown that this approach is often ineffective when applied to domain-specific tasks such as clinical NER [21; 22]. While some efforts have focused on prompt tuning to achieve acceptable performance, these methods typically require extensive and meticulous prompt design, which may not generalize well across clinical settings [23]. Despite the potential advantages of NLP in enhancing maternal morbidity, there remains a scarcity of research and knowledge regarding its application in this field, particularly in low-resource settings and Spanish-speaking countries. Particularly, there is a lack of corpora in Spanish associated with specific medical fields to train or fine-tune deep learning models. To the best of the authors' knowledge, there are no models adjusted to named entity recognition associated with maternal EHR, neither in English nor in Spanish. To address this gap, we have constructed a new dataset comprising sentences in Spanish extracted from maternal EHRs. This study aims to explore the use of pre-trained transformer-based models, which are fine-tuned using our dataset, to extract clinical concepts related to maternal electronic health records. Our hypothesis is that NLP has the capability to enhance maternal health outcomes by assisting healthcare providers in making well-informed decisions, identifying high-risk patients, and improving the overall quality of maternal care.