Published March 2, 2021
| Version 0.2
Dataset
Open
Reproducibility in Machine Learning and Healthcare Paper Annotation Datasets
- 1. MIT
- 2. Shirly
- 3. Nikki
- 4. Rajesh
- 5. Luca
- 6. Marzyeh
Description
Paper Annotations for an extended version of https://arxiv.org/abs/1907.01463
Artificial intelligence (AI) and machine learning (ML) for healthcare (ML4H)must be reproducible for reliable clinical use. We evaluate over 200 ML4Hresearch papers and find that health compares poorly to other application areas for AI and ML, particularly concerning data and code accessibility, and propose recommendations for reproducible research
Files
annotated_papers.csv
Files
(465.8 kB)
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md5:ab7629b5d908045c723246302607d518
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md5:751f1dc6f53982e52b08ffedb85a0923
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md5:8d315fabc57d67c0f91f8c45a38a99b2
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md5:203a8cb6415cd7a8bfdf08963fb80f12
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md5:b80e3f43f8e1e4c311260913e2594bd6
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