基于数据的方法来量化使用贝叶斯网络对医疗器械的信任
Mini Thomas1, Omar Boursalie2, Reza Samavi2,3
1Department of Computing and Software, McMaster University, Hamilton, ON L8S 4L8, Canada.
Experimental biology and medicine (Maywood, N.J.)
|January 28, 2024
概括
本研究引入了一种数据驱动的方法来评估使用贝叶斯网络对可穿戴医疗设备的信任. 该方法通过从设备数据中提取概率来量化信任,从而使可靠的可信度评估成为可能.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 可靠性工程可靠性工程
背景情况:
- 贝叶斯网络对于量化信任等主观概念中的不确定性是有价值的.
- 评估对可穿戴医疗设备的信任对于用户采用和数据完整性至关重要.
- 现有的方法可能缺乏基于数据的方法,用于从设备指标直接估计参数.
研究的目的:
- 提出一种新的数据驱动方法来估计贝叶斯网络参数,用于可穿戴医疗设备的信任量化.
- 开发一种方法,直接从设备数据 (例如传感器质量) 中提取信任因子概率.
- 为了建立一个相对的信任分数来比较不同的设备在相同的条件下.
主要方法:
- 开发了一种数据驱动的方法来估计贝叶斯参数,使用从可穿戴设备数据中提取的概率.
- 综合专家知识,以确定信任因素之间的关系的强度.
- 应用了从需求工程的传播规则来计算基于个别因素贡献的信任得分.
主要成果:
- 成功开发和评估贝叶斯网络,用于对来自两个制造商的类似可穿戴设备的信任量化.
- 在相同的测试条件和噪音水平下证明了拟议方法的可学习性.
- 展示了在不同设备中评估可信度的方法的普遍性.
结论:
- 拟议的数据驱动贝叶斯网络方法有效量化了对可穿戴医疗设备的信任.
- 该方法允许从设备性能指标直接估计信任因素.
- 该方法为评估设备可信度提供了一个可靠和可泛化的框架.
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