The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data
Author(s)Citi, Luca; Ghassemi, Marzyeh; Celi, Leo Anthony G.; Pollard, Tom Joseph
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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.
DepartmentMassachusetts Institute of Technology. Institute for Medical Engineering & Science; Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Public Library of Science
Celi, Leo A., Luca Citi, Marzyeh Ghassemi, and Tom J. Pollard. “The PLOS ONE Collection on Machine Learning in Health and Biomedicine: Towards Open Code and Open Data.” Edited by Leonie Anna Mueck. PLOS ONE 14, no. 1 (January 15, 2019): e0210232. © 2019 Celi et al.
Final published version