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The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data
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 patie...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333339/ https://www.ncbi.nlm.nih.gov/pubmed/30645625 http://dx.doi.org/10.1371/journal.pone.0210232 |
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author | Celi, Leo A. Citi, Luca Ghassemi, Marzyeh Pollard, Tom J. |
author_facet | Celi, Leo A. Citi, Luca Ghassemi, Marzyeh Pollard, Tom J. |
author_sort | Celi, Leo A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6333339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63333392019-01-31 The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data Celi, Leo A. Citi, Luca Ghassemi, Marzyeh Pollard, Tom J. PLoS One Overview 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. Public Library of Science 2019-01-15 /pmc/articles/PMC6333339/ /pubmed/30645625 http://dx.doi.org/10.1371/journal.pone.0210232 Text en © 2019 Celi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Overview Celi, Leo A. Citi, Luca Ghassemi, Marzyeh Pollard, Tom J. The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data |
title | The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data |
title_full | The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data |
title_fullStr | The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data |
title_full_unstemmed | The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data |
title_short | The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data |
title_sort | plos one collection on machine learning in health and biomedicine: towards open code and open data |
topic | Overview |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333339/ https://www.ncbi.nlm.nih.gov/pubmed/30645625 http://dx.doi.org/10.1371/journal.pone.0210232 |
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