语言建模使用自我报告问卷选帕金森病
Diego Machado Reyes1, Juergen Hahn1, Li Shen2
1* Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th St, Troy, 12180, NY, USA.
medRxiv : the preprint server for health sciences
|October 14, 2024
概括
一个新的人工智能 (AI) 模型Quest2Dx分析了用于早期发现帕金森病 (PD) 的健康问卷. 这种非侵入性工具显示出高精度,为初级保健查提供了一个有前途的解决方案.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 帕金森病 (PD) 构成了重大的公共卫生挑战,特别是在老年人群中.
- 目前的PD诊断方法通常依赖于运动症状和侵入性手术,阻碍了早期检测.
- 开发可访问的,非侵入性的PD早期诊断工具至关重要.
研究的目的:
- 建立一个可转移的人工智能 (AI) 模型,名为Quest2Dx,用于使用健康问卷对帕金森病进行非侵入性诊断.
- 解决人工智能模型开发中的挑战,例如缺少的数据和对问卷特定建模的需求.
- 提高AI模型在疾病诊断中的可解释性.
主要方法:
- 开发了Quest2Dx,一种用于分析健康问卷数据的新型语言建模方法.
- 实施了一个可转移的AI模型,旨在跨不同问卷工作并处理缺失的答案.
- 在PPMI和Fox Insight数据集上验证了Quest2Dx.
主要成果:
- Quest2Dx实现了高的诊断准确性,接收器操作特征曲线下的区域 (AUROC) 分数为0.977 (PPMI) 和0.974 (福克斯洞察).
- 证明了强大的交叉问卷验证表现,实现了0.920的AUROC (PPMI到Fox Insight) 和0.952 (Fox Insight到PPMI).
- 确定了关键的预测问题,为PD指标提供了洞察力.
结论:
- Quest2Dx代表了低成本,非侵入性帕金森病查的重大进步.
- 该模型的可转移性和可解释性为在初级保健机构中检测PD提供了一个有希望的方法.
- 这种人工智能驱动的工具有可能改善帕金森病的早期诊断和管理.
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