PDF(Pubmed)
Abstract：
The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for example, avoid instances of missing information. In this Editorial, the editors of JMIR Publications journals discuss the general JMIR Publications policy regarding authors\' application of reporting guidelines and specifically focus on the reporting of ML studies in JMIR Publications journals, using the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines, with an example of how authors and other journals could use the CREMLS checklist to ensure transparency and rigor in reporting.
摘要：
提交并发表在《医学互联网研究杂志》和其他JMIR出版物期刊上的机器学习（ML）模型的论文数量稳步增加。参与此类手稿审查过程的编辑和同行审稿人经常经历多个审查周期，以提高报告的质量和完整性。使用报告指南或清单可以帮助确保提交（和出版）的科学手稿质量的一致性，例如,避免丢失信息的实例。在这篇社论中，JMIR出版物期刊的编辑讨论了关于作者应用报告指南的一般JMIR出版物政策，并特别关注JMIR出版物期刊中ML研究的报告，使用机器学习研究合并报告(CREMLS)指南，作者和其他期刊如何使用CREMLS清单来确保报告的透明度和严谨性。
