MIT Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, United States of America.
School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.
PLoS One. 2019 Jan 15;14(1):e0210232. doi: 10.1371/journal.pone.0210232. eCollection 2019.
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.
近年来，随着医疗环境的数字化和越来越多可用于分析的计算机系统的出现，健康和生物医学领域的机器学习研究呈爆炸式增长。我们许多人都相信，这些发展将显著改善患者护理。然而，与许多学术学科一样，进展受到缺乏代码和数据共享的阻碍。在将这组关于健康和生物医学领域机器学习的 PLOS ONE 论文汇集在一起时，我们试图强调可重复性的重要性，尽可能要求作者在提交论文的同时分享数据和代码。
