The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data.

Journal: PloS one
Published Date: Jan 15, 2019

Abstract

Recent years have seen a surge of studies in machine learning in health and biomedicine, driven by digitalization of healthcare environments and increasingly accessible computer systems for conducting analyses. Many of us believe that these developments will lead to significant improvements in patient care. Like many academic disciplines, however, progress is hampered by lack of code and data sharing. In bringing together this PLOS ONE collection on machine learning in health and biomedicine, we sought to focus on the importance of reproducibility, making it a requirement, as far as possible, for authors to share data and code alongside their papers.

Authors

  • Leo A Celi
    Beth Israel Deaconess Medical Center, Pulmonary Division and Harvard Medical School, Boston, MA 02215, USA.
  • Luca Citi
    School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.
  • Marzyeh Ghassemi
    Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Tom J Pollard
    MIT Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, United States of America.

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