Infant feeding practice and associated factors among hiv-positive mothers attending anti-retroviral therapy services in health facilities of Bahir Dar City.

The digitization of health services and clinical processes has resulted in the healthcare industry generating an ever-increasing amount of textual data, encompassing electronic health records, clinical notes, medical reports, and discharge letters among many others. While structured data is frequently used for health economics and registries, the aforementioned unstructured clinical narratives are preferred by physicians to record patients’ clinical information due to their flexibility and efficiency, and account for up to 40% of the data generated in current hospital systems [, ]. The substantial potential of narrative text data to support clinical applications was recognized early [–] and more recently, research efforts have been directed towards developing medical applications assisted by artificial intelligence (AI). Prominent applications include decision support systems that assist healthcare professionals in their tasks, alleviating their workload and providing better treatments for patients []. However, the unstructured nature of textual data and the intricacies of the biomedical field pose significant challenges for leveraging its potential. In such a context, Natural Language Processing (NLP) methods could structure that information to support downstream clinical applications. Recent advancements in NLP brought about by large-scale pre-trained language models based on the Transformer [] architecture have introduced new ways for extracting and analyzing the knowledge contained within the clinical texts. Through extensive self-supervised training on vast corpora of text, a model can acquire valuable representations of a language, producing highly effective language models. The success of Transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) [] and its improved version Robustly optimized BERT Pretraining approach (RoBERTa) [], can be largely attributed to the use of transfer learning expressed in the pretrain-finetune paradigm. In this paradigm, a model initially goes through a resource-intensive training process, i.e. , using general-purpose textual data to learn the language structure. This pre-training phase is self-supervised, eliminating the need for labeled data by utilizing objectives like masked language modeling []. The model is then for various tasks through a second, more cost-effective training round using a smaller, labeled, and task-specific dataset that adjusts the model’s weights to fit the specific task and application domain at hand.