Feasibility study on a Noninvasive Assessment of ALS Patient Emotional State
Garbey, M.; Lesport, Q.; Oztosun, G.; Heidebrecht, M.; Pirouz, K.; Bayat, E.
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This study addresses the need for objective, real-time assessment of emotional responsiveness and coping strategies in individuals with Amyotrophic Lateral Sclerosis (ALS) to support personalized care. We are using non-invasive speech analysis and data science methods on an expanded cohort comprising 28 ALS patient visits. We first demonstrate that commonly available artificial intelligence tools, including current-generation large language models (LLMs), such as ChatGPT, Gemini and Claude, do not provide reliable or reproducible assessments of patient concern levels in the absence of expert clinical supervision. Further, we observe a discrepancy between subjective and objective metrics such as the forced vital capacity for breathing. We introduce a novel functional classification system that contextualizes clinician-rated emotional concern relative to the patient's functional impairment as measured by the ALS Functional Rating Scale (ALS-FRS). Patient responses are categorized as: Congruent: Emotional responsiveness is proportional to functional impairment. Muted: Emotional response is lower than expected given functional impairment. Excessive: Emotional response exceeds that expected given functional impairment
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