Intelligent Filtering in Biomedical Literature Retrieval Systems

Abstract

The ever-increasing mass of biomedical literature is becoming a greater burden to a researcher attempting to obtain information pertinent to them successfully. Basic retrieval techniques do not offer the necessary amount of accuracy and detailed information, and tend to flood the user, which more advanced techniques can alleviate. The use of intelligent filtering based on abbreviations of natural language processing (NLP), machine learning (ML) and other fields of artificial intelligence (AI) has become the main problem in searching biomedical literature, and greatly increased the availability of information. The filtering of large datasets using these algorithms, domain-specific ontologies, and user intent modeling allows users to access the literature that addresses their research requirements. Others rely on entity recognition, citation analysis, and feedback analysis in improving relevance. Deep learning systems that use semantic processing of biomedical text, like BERT and BioBERT, have enhanced the relevance, contextual sensitivity, and efficiency of literature search. This paper analyzes the current intelligent filtering methods, their effectiveness, and other issues such as data discrepancies, variability of phrases, and logical reasoning, among others. Cognitive overload, research, and rapid discovery can be managed by integrating smart filters into biomedical literature retrieval systems. The additional advancement of these systems may lead to considerable changes in the manner in which biomedical information is obtained and used in the academic world and health care organizations.