Reproducibility in machine learning for health research: Still a ways to go-Reference-Cited by-同舟云学术

Reproducibility in machine learning for health research: Still a ways to go

Published:2021-03-24 Issue:586 Volume:13 Page:
ISSN:1946-6234
Container-title:Science Translational Medicine
language:en
Short-container-title:Sci. Transl. Med.

Author:

McDermott Matthew B. A.¹^ORCID,Wang Shirly²³,Marinsek Nikki⁴,Ranganath Rajesh⁵,Foschini Luca⁴^ORCID,Ghassemi Marzyeh²⁶⁷^ORCID

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

2. Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada.

3. Layer 6 AI, TD Bank Group, Toronto, ON M5G 1M1, Canada.

4. Evidation Health Inc., San Mateo, CA 94402, USA.

5. Center for Data Science and Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10011, USA ; Department of Population Health, NYU Langone Health, New York, NY, USA 10016.

6. Vector Institute, University of Toronto, Toronto, ON M5G 1M1, Canada.

7. Department of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada.

Abstract

Machine learning applied to health falls short on several reproducibility metrics compared to other machine learning subfields.

Funder

National Institutes of Health

Canadian Institute for Advanced Research

Natural Sciences and Engineering Research Council of Canada

Mitacs

National Institutes of Health National Institute of Mental Health

Publisher

American Association for the Advancement of Science (AAAS)

Subject

General Medicine

Reference30 articles.

1. 1,500 scientists lift the lid on reproducibility

2. Retraction rates are on the rise

3. O. E. Gundersen S. Kjensmo State of the art: Reproducibility in artificial intelligence Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (2018) vol. 32.

4. Challenges to the Reproducibility of Machine Learning Models in Health Care

5. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

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