RESUMEN

In the ever-evolving landscape of medical research and healthcare, the abundance of scientific articles presents both a treasure trove of knowledge and a daunting challenge. Researchers, clinicians, and data scientists grapple with vast amounts of unstructured information, seeking to extract meaningful insights that can drive advancements in the biomedical domain including, research trends, patient care, drug discovery, and disease understanding. This paper utilizes the topic extraction algorithms on Breast Cancer Research to shed light on the current trends and the path to follow in this field. We utilized TextRank and Large Language Models (LLM) using the TripleA tool to extract topics in the field, analyzing and comparing the results.

Asunto(s)

Neoplasias de la Mama , Procesamiento de Lenguaje Natural , Humanos , Minería de Datos/métodos , Femenino , Investigación Biomédica , Algoritmos , Publicaciones Periódicas como Asunto