A RECENT SURVEY ON TEXT MINING TOOLS & TECHNIQUES IN THE BIO-MEDICAL DOMAIN

Abstract [English]

Text mining in the biomedical domain has emerged as a crucial technique that capitalizes on the advancements of natural language processing and machine learning methodologies to extract valuable insights from the biomedical texts available to us from different sources. This research paper explores the methods and recent advancements in text mining techniques for the biomedical field. We analyze various approaches, including Named Entity Recognition (NER), information retrieval, and machine learning algorithms, focusing on their application to biomedical data.
Biomedical text mining aims to extract valuable information from a vast and diverse array of unstructured medical text data available from different sources, which can significantly contribute to the advancements in medical research and healthcare improvement. This research paper reflects a comprehensive exploration of fundamental concepts of biomedical textual data mining, its techniques, software, applications of biomedical textual data mining, a literature survey on clinical text mining, and its key challenges. It examines the effectiveness of these techniques in identifying and categorizing biomedical entities like genes, proteins, diseases, and drugs. Through case studies and empirical evaluations, we demonstrate how text mining contributes to knowledge discovery, improves data management, and supports decision-making in healthcare and research. This study aims to provide a detailed overview of state-of-the-art biomedical text mining, offering insights into future directions and potential improvements in this rapidly evolving field.