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Semantic Scholar Open Access 2021 241 sitasi

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

Matthew B. A. McDermott Shirly Wang N. Marinsek R. Ranganath L. Foschini +1 lainnya
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Abstrak

Machine learning applied to health falls short on several reproducibility metrics compared to other machine learning subfields. Machine learning for health must be reproducible to ensure reliable clinical use. We evaluated 511 scientific papers across several machine learning subfields and found that machine learning for health compared poorly to other areas regarding reproducibility metrics, such as dataset and code accessibility. We propose recommendations to address this problem.

Topik & Kata Kunci

Medicine

Penulis (6)

M

Matthew B. A. McDermott

S

Shirly Wang

N

N. Marinsek

R

R. Ranganath

L

L. Foschini

M

M. Ghassemi

Format Sitasi

APA MLA BibTeX
McDermott, M.B.A., Wang, S., Marinsek, N., Ranganath, R., Foschini, L., Ghassemi, M. (2021). Reproducibility in machine learning for health research: Still a ways to go. https://doi.org/10.1126/scitranslmed.abb1655

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Informasi Jurnal
Tahun Terbit
2021
Bahasa
en
Total Sitasi
241×
Sumber Database
Semantic Scholar
DOI
10.1126/scitranslmed.abb1655
Akses
Open Access ✓