数据集专家对健康重要特征的看法 数据集准备用于机器学习:定性研究
Madelena Y Ng1,2, Alaa Youssef3, Adam S Miner4
1Department of Medicine (Biomedical Informatics), Stanford University School of Medicine, Stanford, California.
JAMA network open
|December 1, 2023
概括
开发人工智能 (AI) 准备好的健康数据需要专家的洞察力. 一个新的框架强调了需要准确性,道德考虑和实际约束来改善医疗保健中的AI.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 缺乏数据质量框架限制了为医疗保健开发人工智能 (AI) 准备数据集的发展.
- 这阻碍了机器学习 (ML) 研究和临床AI应用的诊断能力.
研究的目的:
- 确定专家对高质量,有用的数据集的认知,用于健康和生物医学机器学习 (ML) 研究.
- 开发一个框架来评估健康数据集中的AI准备程度.
主要方法:
- 定性研究涉及20名健康数据集创建者和ML研究人员的半结构面试.
- 有目的的抽样确保了16个健康数据集/数据库的多样化代表性.
- 专题分析用于分析采访数据,并补充了调查信息的内容分析.
主要成果:
- 开发了一个AI准备框架,整合了3个主要主题和11个子主题.
- 专家将准确性,完整性,一致性和适应性确定为关键的评估要素.
- 伦理获取和社会影响成为关键考虑因素,与数据可用性,质量标准,文档,团队科学和激励一起.
结论:
- 卫生数据集中的AI准备要求对多个要素进行全面评估,平衡透明度和伦理与实际约束.
- 对数据集创建实践的更新对于在患者护理中推进可靠,相关和道德的AI和ML应用至关重要.
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