Healthsheet：为健康数据集开发透明的数据库
Healthsheet: Development of a Transparency Artifact for Health Datasets
摘要：
机器学习（ML）方法已在广泛的医疗保健应用中展示出令人鼓舞的结果。数据在开发直接影响人们生活的基于ML的医疗保健系统方面发挥着至关重要的作用。围绕在医疗保健中使用ML的许多伦理问题都源于我们收集、使用和处理数据的方式中存在的结构性不平等。因此，制定指导方针以改进有关创建、使用和维护ML医疗数据集的文档实践至关重要。在这项工作中，我们介绍了健康表，一个上下文适应的原始问卷~ \cite{gebru2018datasheets}健康特定的应用程序。通过一系列半结构化的访谈，我们调整了医疗保健数据文件的说明书。作为Healthsheet开发过程的一部分，为了了解研究人员在创建电子表格时面临的障碍，我们使用了三个公开的医疗保健数据集作为案例研究，每个数据集都有不同类型的结构化数据：电子健康记录（EHR），临床试验研究数据和基于智能手机的性能结果测量。我们从受访者研究和案例研究中的发现表明：1）传单应该在医疗保健的背景下，2）尽管有采取问责制做法的激励措施，如传单，在更广泛地使用这些实践方面缺乏一致性3）卫生界的ML如何将数据表，特别是\textit{Healthsheets}视为诊断工具，以揭示数据集的局限性和优势4）不同领域对医疗保健关注的相对重要性。
英文摘要：
Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role in developing ML-based healthcare systems that directly affect people's lives. Many of the ethical issues surrounding the use of ML in healthcare stem from structural inequalities underlying the way we collect, use, and handle data. Developing guidelines to improve documentation practices regarding the creation, use, and maintenance of ML healthcare datasets is therefore of critical importance. In this work, we introduce Healthsheet, a contextualized adaptation of the original datasheet questionnaire ~\cite{gebru2018datasheets} for health-specific applications. Through a series of semi-structured interviews, we adapt the datasheets for healthcare data documentation. As part of the Healthsheet development process and to understand the obstacles researchers face in creating datasheets, we worked with three publicly-available healthcare datasets as our case studies, each with different types of structured data: Electronic health Records (EHR), clinical trial study data, and smartphone-based performance outcome measures. Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly \textit{Healthsheets} as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns.
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