English(EN) TAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive Impairment

新AI框架TAMI改善老年人心理健康评估

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了TAMI，一个新颖的多模态融合框架，旨在改善轻度认知障碍（MCI）老年人抑郁和焦虑的评估。TAMI通过时间对齐语音、语言、面部和生理特征，处理跨模态的缺失数据，并提供临床见解的可解释性，从而解决了现有方法的局限性。在对49名参与者进行的试验中，TAMI在抑郁症方面的AUROC得分为0.68，在焦虑症方面的得分为0.69，其中时间对齐被证明是性能最显著的增强因素。 AI

影响这项研究可能为弱势群体带来更具可扩展性和准确性的心理健康筛查工具。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Merna Bibars, Bolaji Omofojoye, Allan I. Levey, Rachel Hershenberg, Gari D. Clifford, Hyeokhyen Kwon · 2026-09-01 04:00

TAMI：老年轻度认知障碍患者的心理健康评估的时间对齐、缺失感知和可解释的多模态融合

arXiv:2608.30857v1 Announce Type: cross Abstract: Depression and anxiety in older adults with Mild Cognitive Impairment (MCI) are frequently underdiagnosed due to limited access to care. Multimodal analysis of remote clinical interviews is a scalable screening approach, but exist…

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TAMI：老年轻度认知障碍患者的心理健康评估的时间对齐、缺失感知和可解释的多模态融合

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