为医学数据集设计标准化文档框架，助研究者高效准备机器学习数据。
Datasheets for AI and medical datasets (DAIMS): a data validation and documentation framework before machine learning analysis in medical research
- 提出DAIMS框架，包含24项数据标准化检查项
- 提供工具自动验证部分标准，生成数据字典与分析流程图
- 适合医学研究中需应用机器学习的团队使用
尽管数据工程取得进展，医学机器学习研究中仍存在数据验证与文档规范不一致的问题。为此，我们扩展了“Datasheets for Datasets”框架，提出“Datasheets for AI and medical datasets - DAIMS”。DAIMS 提供包含24项常见数据标准化要求的检查清单，配套软件工具可自动校验其中部分条款；还包含扩展版数据文档表单、研究问题映射表、数据字典表格及引导分析路径的流程图。该框架可作为医学数据标准化参考，并为研究者规划有效机器学习分析提供路线图。DAIMS 已开源于 GitHub，并提供在线应用，支持自动化评估关键数据质量环节，提升机器学习研究的数据准备效率。
原文摘要 · Abstract (English)
Despite progresses in data engineering, there are areas with limited consistencies across data validation and documentation procedures causing confusions and technical problems in research involving machine learning. There have been progresses by introducing frameworks like "Datasheets for Datasets", however there are areas for improvements to prepare datasets, ready for ML pipelines. Here, we extend the framework to "Datasheets for AI and medical datasets - DAIMS." Our publicly available solution, DAIMS, provides a checklist including data standardization requirements, a software tool to assist the process of the data preparation, an extended form for data documentation and pose research questions, a table as data dictionary, and a flowchart to suggest ML analyses to address the research questions. The checklist consists of 24 common data standardization requirements, where the tool checks and validate a subset of them. In addition, we provided a flowchart mapping research questions to suggested ML methods. DAIMS can serve as a reference for standardizing datasets and a roadmap for researchers aiming to apply effective ML techniques in their medical research endeavors. DAIMS is available on GitHub and as an online app to automate key aspects of dataset evaluation, facilitating efficient preparation of datasets for ML studies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。