BITS Faculty Publications

Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867

Browse

Subject: search.filters.subject.Biomedical

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    From information overload to lucidity: a survey on leveraging gpts for systematic summarization of medical and biomedical artifacts
    (IEEE, 2024-12) Chalapathi, G.S.S.; Singh, Amit Rajnarayan
    In medical research, the rapid proliferation of condition-specific studies has led to an information overload, making it challenging for researchers and practitioners to stay abreast of the latest findings. This paper presents a comprehensive survey on leveraging Generative Pretrained Transformers (GPTs) to summarize medical and biomedical artifacts systematically. We delve into the current applications of GPTs in this domain, discussing their role in understanding and summarizing research papers, medical dialogues, and medical records. Through a comparative analysis of recent studies and methodologies, we highlight the effectiveness of GPTs in distilling complex medical information into concise, understandable summaries. Our survey underscores the potential of GPTs as a tool for navigating the information overload in medical research and bringing clarity to healthcare professionals. This transformation will enhance patient care and outcomes, such as improving the accessibility and comprehensibility of medical research, assisting in rapid information retrieval, and facilitating the summarization of complex medical studies for broader audiences.
  • No Thumbnail Available
    Item
    A Utility Tool for Personalised Medicine
    (ACM Digital Library, 2018) Gavankar, Chetana
    Biomedical research is drowning in data, yet starving for knowledge. As the volume of scientific literature is growing unprecedentedly, revolutionary measures are needed for data management. Accessibility, analysis and mining knowledge from this textual data has become a very important task. One such source is NCBI that houses a series of databases (PubMed) relevant to biotechnology and bio-medicine. It is an important resource for bioinformatics tools and services. In this paper, a system is proposed that encases all the biomedical articles of PubMed as needed by bioinformaticians. Using machine learning and natural language processing, the tool aims at assisting clinicians and biomedical researchers to understand and graphically represent the relevance of gene in a given disease context. It will also support entity-specific bio-curation searches to get a list of most effective drugs for a particular disease. The system is evaluated by using standard information retrieval measures namely, Precision, Recall and F-score to measure the relevance of search results.