Machine learning algorithms designed to characterize, monitor, and intervene
on human health (ML4H) are expected to perform safely and reliably when
operating at scale, potentially outside strict human supervision. This
requirement warrants a stricter attention to issues of reproducibility than
other fields of machine learning.
In this work, we conduct a systematic evaluation of over 100 recently
published ML4H research papers along several dimensions related to
reproducibility. We find that the field of ML4H compares poorly to more
established machine learning fields, particularly concerning data and code
accessibility. Finally, drawing from success in other fields of science, we
propose recommendations to data providers, academic publishers, and the ML4H
research community in order to promote reproducible research moving forward.

本文通过对 100 多篇机器学习应用于人类健康领域的研究论文进行系统评估，发现相较于其他机器学习领域，该领域在数据和代码可获取性等可重复性方面表现较差，因此提出了对数据提供者、学术出版商和 ML4H 研究社区的建议，以促进可重复性研究的发展。