WATCH-SS: A Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech
Pugh, S.; Hill, M.; Hwang, S.; Wu, R.; Jang, K.; Iannone, S. L.; O'Connor, K.; O'Brien, K.; Eaton, E.; Johnson, K. B.
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Early detection of cognitive impairment (CI) is critical for timely intervention in Alzheimers disease and AD-related dementias. To address this, we propose the Warning Assessment and Alerting Tool for Cognitive Health from Spontaneous Speech (WATCH-SS), a modular and explainable three-stage framework for detecting CI from a patients speech sample. The framework uses detectors for five linguistic and acoustic indicators of CI, aggregates their outputs into a set of clinically interpretable summary features, and uses a predictive model for CI classification. We consider multiple approaches to implementing these detectors that range from simple, computationally efficient methods suitable for real-time analysis to strong, resource-intensive methods, better for high accuracy offine analysis. On the DementiaBank ADReSS dataset, WATCH-SS achieved strong predictive performance (AUC = 80% on the test set). This work demonstrates that a modular, feature-based approach can achieve strong performance while providing a transparent diagnostic profile, representing a significant step towards a trustworthy and clinically-usable screening tool for primary care.
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