CITATION.cff

cff-version: 1.2.0
title: >-
  Unveiling Medical Insights: Advanced Topic Extraction from
  Scientific Articles
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Ehsan
    family-names: Bitaraf
    affiliation: >-
      Rajaie Cardiovascular Medical and Research Center,
      Iran University of Medical Sciences, Tehran, Iran
    orcid: 'https://orcid.org/0000-0002-6588-7349'
  - given-names: Maryam
    family-names: Jafarpour
    affiliation: >-
      Center for Medical Data Science, Medical University of
      Vienna, Vienna, Austria
    orcid: 'https://orcid.org/0000-0001-7266-5018'
  - given-names: Sina
    family-names: Shool
    affiliation: >-
      Rajaie Cardiovascular Medical and Research Center,
      Iran University of Medical Sciences, Tehran, Iran
    orcid: 'https://orcid.org/0000-0002-0280-3187'
  - given-names: Reza
    family-names: 'Saboori Amleshi '
    affiliation: >-
      Rajaie Cardiovascular Medical and Research Center,
      Iran University of Medical Sciences, Tehran, Iran
    orcid: 'https://orcid.org/0000-0002-0299-5027'
identifiers:
  - type: doi
    value: 10.6084/m9.figshare.25533532
    description: 'dataset '
  - type: url
    value: 'https://pubmed.ncbi.nlm.nih.gov/39176947/'
    description: Article
repository-code: 'https://github.com/mjafarpour87/medical-insights'
abstract: >-
  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.
keywords:
  - Topic Extraction
  - Natural Language Processing
  - Large Language Models
  - Text Rank
  - Bibliometric Analysis
  - Breast Cancer
license: MIT