在远程帕金森病人工智能评估中量化设备类型和手性偏差
Zerin Nasrin Tumpa1, Md Rahat Shahriar Zawad1, Lydia Sollis1
1University of Hawaii at Manoa, Honolulu, HI, USA.
NPJ digital medicine
|August 27, 2025
概括
机器学习模型可以通过手动预测帕金森病 (PD). 然而,设备类型和使用方式的偏差可能会影响数字健康工具的诊断准确性.
科学领域:
- 数字健康
- 机器学习
- 神经学
背景情况:
- 算法公平性和数字健康公平性是人工智能驱动的医疗保健的关键问题.
- 机器学习 (ML) 模型越来越多地用于疾病预测,包括帕金森病 (PD).
- 用手指和手部运动进行结构化的评估,可提供PD检测的潜在数据.
研究的目的:
- 在预测PD的ML模型中调查算法公平性和数字健康公平性.
- 评估人口偏差,设备类型和手性对PD预测准确性的影响.
- 识别远程数字健康诊断中的潜在偏差.
主要方法:
- 收集了251名参与者的数据 (99名患有PD或怀疑PD,152名没有).
- 使用随机森林模型基于手指和手动数据进行PD预测.
- 使用精度,AUROC,灵敏度,特异性和F1分数来评估模型的性能.
- 检查人口群体,设备类型和使用不同影响和均等机会的偏见.
主要成果:
- 随机森林模型实现了92%的准确性,94%的AUROC,86%的灵敏度,92%的特异性和84%的F1得分.
- 仅检查F1分数之间的差异时,没有发现显著的偏差.
- 对于设备类型和主导手,发现了偏差,影响了正确预测和错误率.
- 性别和种族对PD预测没有显著的影响.
结论:
- 对于PD的远程数字健康诊断可能含有未知的偏差.
- 手性和设备特征可能引入偏见,并可能作为社会经济因素的代理.
- 解决这些偏见对于确保对神经疾病提供公平的数字健康解决方案至关重要.
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