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

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  • Leo A Celi
  • Luca Citi
  • Marzyeh Ghassemi
  • Tom J Pollard

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.

Suggested Citation

  • Leo A Celi & Luca Citi & Marzyeh Ghassemi & Tom J Pollard, 2019. "The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-7, January.
    • Handle: RePEc:plo:pone00:0210232
    DOI: 10.1371/journal.pone.0210232
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