ML-Net: multi-label classification of biomedical texts with deep neural networks.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Nov 1, 2019

Abstract

OBJECTIVE: In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use. We propose ML-Net, a novel end-to-end deep learning framework, for multi-label classification of biomedical texts.

Authors

  • Jingcheng Du
    University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Qingyu Chen
    Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
  • Yifan Peng
    Department of Population Health Sciences, Weill Cornell Medicine, New York, USA.
  • Yang Xiang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Cui Tao
    The University of Texas Health Science Center at Houston, USA.
  • Zhiyong Lu
    National Center for Biotechnology Information, Bethesda, MD 20894 USA.

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