Clinical Context-Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation.

Journal: Journal of medical Internet research
Published Date: Oct 23, 2020

Abstract

BACKGROUND: Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization.

Authors

  • Muhammad Afzal
    Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. muhammad.afzal@oslab.khu.ac.kr.
  • Fakhare Alam
    Department of Computer Science & Engineering, School of Engineering and Computer Science, Oakland University, Rochester, MI, United States.
  • Khalid Mahmood Malik
    Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA. Electronic address: mahmood@oakland.edu.
  • Ghaus M Malik
    Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, United States.

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