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Marceli Wac1,2, Raul Santos-Rodriguez1, Chris McWilliams1,2
1Faculty of Engineering, University of Bristol, Bristol, United Kingdom.
临床医生需要灵活,连续的工作流程在医疗保健数据注释. 一个新的数字工具应该支持灵活的分析,标签创建和任务之间的无过渡,以更好地整合机器学习.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的机器学习
- 数据注释数据注释
背景情况:
- 计算方法和机器学习在医疗保健领域具有潜力,但在数据注释方面面临挑战.
- 数据注释需要领域专业知识和时间,而临床医生往往缺乏这些资源.
- 目前的工具对于临床数据注释工作流程是不够的.
研究的目的:
- 调查重症监护室 (ICU) 工作人员如何处理数据注释.
- 确定针对医疗保健机构量身定制的数字数据注释工具的要求.
主要方法:
- 一项实验活动涉及7名ICU临床医生,他们注释了时间序列入院数据.
- 参与者在45分钟的研讨会中注释了从机械通风中断奶的时间.
- 用诺曼的交互周期来定义软件要求,分析了临床医生的行为.
主要成果:
- 临床医生使用循环过程:调查,注释,重新评估和改进.
- 在四个领域确定了数字工具的11个要求:个人入学注释,半自动注释,操作约束和机器学习标签使用.
- 具体需求包括分析和标签创建的灵活性,以及工作流的连续性.
结论:
- 临床数据注释的成功取决于灵活的分析和标签创建.
- 跨多个录取的工作流连续性对于高效的注释至关重要.
- 需要在数据调查,注释和标签改进之间进行无过渡.
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