## 1 Introduction  
Following the achievement of the United Nations' Millennium Development Goals (MDGs), the Sustainable Development Goals (SDGs) were established to mobilize action across all nations and address a wide spectrum of social requirements. Among these goals, enhancing maternal health and reducing maternal mortality continue to be a significant priority for the global community. Maternal mortality is a sentinel event and refers to the death of a woman during pregnancy, childbirth, or within 42 days of the end of the pregnancy [1]. Maternal Mortality Ratio (MMR) has been identified as an important indicator of the quality of medical care and an indicator of progress in the international development goals. Despite a decrease in global maternal mortality during the last decade, rates remain unacceptable. According to the World Health Organization (WHO), the estimated global MMR in 2020 was 220 deaths per 100,000 live births, which is an issue of global concern [2]. However, beyond maternal mortality, it is important to act on the conditions that increase maternal morbidity as a precursor to mortality. Despite the lack of an agreed-upon definition for maternal morbidity [3], the WHO has defined it as \"any health condition attributed to and/or complicating pregnancy, and childbirth that has a negative impact on the woman's well-being and/or functioning\" and estimates that for each maternal death, 20 or 30 women suffer from maternal morbidity. Maternal near-miss (MNM) is an indicator that includes women who survived after presenting a potentially fatal complication during pregnancy, childbirth, or the puerperimum as a case of extremely severe maternal morbidity, a severity that brings women very close to death [4]. More than 18.67/1000 live births of the general population of the world suffered from MNM. However, due to a lack of agreement on the definition of an MNM, comparing the prevalence in different countries or regions is relatively difficult [5]. However, equally important as mortality, this problem affects most middle- and low-income countries, such as those in Africa or Latin America. Authors report an incidence of MMN between 5.9 and 12.3 per 1000 live births in the Latin American and Caribbean region [6]. To address this problem, healthcare systems increasingly turn to technological solutions. One notable advancement is the growing adoption of electronic health records (EHRs). EHRs offer real-time access to a patient's medical history, enabling informed clinical decision-making, improved communication between healthcare providers, and reduced likelihood of medical errors. Using the benefits of EHRs, maternal healthcare can become more efficient and effective, resulting in improved maternal and newborn outcomes [7]. A critical challenge derived from the adoption of EHRs consists in the extraction of relevant information stored in the unstructured clinical text, which has given rise to a task of natural language processing (NLP) known as Named Entity Recognition (NER), becoming an essential task when analyzing and extracting useful information. In this respect, various strategies have emerged, including the use of rule-based approaches, machine learning models, and deep learning. In the fields of natural language processing (NLP), deep learning models have taken on great relevance in recent years, especially those that include the mechanism of self-attention, given the various success cases [8]. These models, which are also known as transformers, have been adapted in specific domains and tasks, as is the case in the clinical domain [9], where transfer learning is very common in adjusting a model with clinical data. However, despite the significant progress made in biomedical NER in the last years, there are limited annotated corpora available for languages other than English and Chinese, which are the most common languages used in biomedical NER. The rise in the popularity of deep learning (DL) models can be attributed to the vast volume of patient data extracted from EHR.