从人类,数据和机器学习的角度来设计个性化自我管理制度
Adrienne Pichon1, Iñigo Urteaga2, Lena Mamykina1
1Columbia University, Department of Biomedical Informatics, USA.
概括
设计智能自我管理系统需要平衡用户需求与AI能力. 这种以人为中心的AI框架将这些因素与更好的个人健康技术结合起来,特别是对于像子宫内膜异位症这样的复杂疾病.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 数字健康数字健康
背景情况:
- 智能系统为患者的自我管理和改善生活质量提供了潜力.
- 设计这些系统是复杂的,需要考虑用户需求,AI能力和算法约束.
- 个人健康技术需要一个整合人力和技术要求的框架.
研究的目的:
- 提出和实施以人为中心的AI框架,用于设计个人健康中的智能系统.
- 在设计过程中调整人类和技术的要求和约束.
- 引导开发有效的基于人工智能的自我管理工具.
主要方法:
- 通过采访的内容分析,利用强化学习概念来引起用户的需求.
- 分析了自动监控应用程序中的"在野外"用户参与日志,以确定数据约束.
- 为子宫内膜异位症自我管理工具收集和三角化人机数据要求.
主要成果:
- 确定了个人健康自我管理工具的特定人,机器学习和数据要求.
- 在现实世界卫生环境中展示了以人为中心的AI框架的应用.
- 通过分析用户参与日志,发现了实际数据限制.
结论:
- 提出的人为中心的人工智能框架有效地使用户需求与人工智能能力和数据约束保持一致.
- 这种方法对于开发智能自我管理系统至关重要,特别是对于 endometriosis 等复杂的慢性疾病.
- 提供了创建符合人类,数据和机器学习视角的系统的建议.
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