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arXiv Machine Learning By Sankalp Vaish, Sumukh Veeramalla, Rishank Singh, Caroline Summerour, John A. Batsis, Xiaohui Liang

CogDrift-4D: Hierarchical Multimodal Modeling of Temporal Behavioral Decay for MCI Detection

Read the original on arXiv Machine Learning →

CogDrift-4D introduces a hierarchical 4D Transformer framework for detecting mild cognitive impairment (MCI) by modeling temporal behavioral decay across seven unscripted multimodal interactions over 18 months. The approach represents continuous, multi‑session data as a dense 4D tensor and employs dual‑level modeling: intra‑session least‑squares regression to capture acute fading of facial and acoustic features, and an inter‑session recursive cross‑attention encoder to accumulate cumulative fatigue. A localized Quality Gate filters low‑integrity features, and early fusion integrates semantic and raw dynamics, achieving an MCI classification accuracy of 0.733 ± 0.13 and an F1‑score of 0.716 ± 0.14, surpassing advanced baselines.

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