The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data
Name
journal.pone.0210232.pdf
Size
357.55 KB
Format
Adobe PDF
Checksum (MD5)
45ad341c35f6483eac6f98cf6589c1e5
Author(s)
Citi, Luca
Ghassemi, Marzyeh
Celi, Leo Anthony G.
Pollard, Tom Joseph
Date Issued
January 2019
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
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.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0210232
