Predicting Future Cognitive Decline from Long-term Observations of Dual-task Performance Data
Keywords: Dual-task, Dementia, MMSE, Early-stage detection, Mild cognitive impairment
TL;DR: we present the first attempt at leveraging long-term observations of dual-task performance data for predicting future cognitive decline
Abstract: Early stage detection of cognitive decline is crucial for effective prevention and treatment of dementia. However, current approaches based on MRI or biomarkers are expensive and impractical, making them unsuitable for early-stage detection from daily measurements. A suitable option is the dual-task paradigm, which involves simultaneously performing two tasks (typically a physical task combined with a cognitive task). This approach has proven effective in assessing daily cognitive status. The underlying principle is that dual-task performance reflects the maximum cognitive load that can be handled by participants, which in turn reflects their current cognitive function. However, a one-time dual-task test cannot predict future changes in cognitive function. In this study, we present the first attempt at leveraging long-term observations of dual-task performance data. Our results show that changes in dual-task performance over time are associated with future cognitive changes. Our approach extracts temporal features from six months of dual-task performance data, and predicts future cognitive decline over the next two years using a machine learning model. Our experimental results yielded an accuracy comparable to that returned by MRI scans, thus demonstrating that the proposed approach can achieve early detection of future cognitive decline from routine dual-task measurements.
Track: 4. AI-based clinical decision support systems
Registration Id: XTNG7P3BPWC
Submission Number: 63
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