应用以人为中心的设计嵌入式机器学习模型与护士一起开发数据标签软件:人对人工智能 (H2AI)
Naomi A Kaduwela1, Susan Horner2, Priyansh Dadar1
1KaviGlobal, 1250 Grove St, Suite 300, Barrington, IL, USA.
International journal of medical informatics
|January 10, 2024
概括
护士们开发了用于疼痛识别的直观软件,使机器学习模型能够准确地标记数据. 这种合作确保了技术满足临床需求,并建立了在患者护理中采用的信任.
科学领域:
- 护理信息学 护理信息学
- 医疗保健中的机器学习
- 人与计算机的交互
背景情况:
- 护士对于急性疼痛管理至关重要,特别是在非语言患者中.
- 护士的专业知识对于开发用于疼痛检测的机器学习 (ML) 模型至关重要.
- 疼痛识别自动监测系统 (PRAMS) 需要护士输入有效的临床决策支持软件 (CDSS).
研究的目的:
- 创建一个直观而高效的数据标签软件,人与人工智能 (H2AI).
- 促进护士参与开发基于 ML 的医疗保健工具.
主要方法:
- 使用了嵌入式机器学习解决方案 (HCDe-MLS) 的以人为中心设计模型.
- 护士使用H2AI软件进行数据标签任务.
- 视频预处理使用了OpenCV,而MobileFaceNet协助了地标放置.
主要成果:
- 开发了H2AI软件,使6名护士能够标记139个视频 (3189张图像).
- 该系统跟踪了评估者之间的可靠性,并存储了用于ML模型训练的标记数据.
- 每张图像的平均标签时间为75秒.
结论:
- 护士参与CDSS开发对于以用户为中心的设计至关重要.
- 涉及护士确保技术与临床优先事项和决策过程保持一致.
- 积极参与促进了信任,并促进了护理实践中的技术采用.
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