2025/12/04 by Raquel Norel, Norel, Raquel, Michele Merler +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Epilepsy research and treatment #FOS: Computer and information sciences #Genomics and Rare Diseases #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2512.04938
openalex publication_date 2025/12/04 · openalex created_date 2025/12/06 · openalex updated_date 2026/07/28
Patients with rare neurological diseases report cognitive symptoms -"brain fog"- invisible to traditional tests. We propose continuous neurocognitive monitoring via smartphone speech analysis integrated with Relational Graph Transformer (RELGT) architectures. Proof-of-concept in phenylketonuria (PKU) shows speech-derived "Proficiency in Verbal Discourse" correlates with blood phenylalanine (p = -0.50, p < 0.005) but not standard cognitive tests (all |r| < 0.35). RELGT could overcome information bottlenecks in heterogeneous medical data (speech, labs, assessments), enabling predictive alerts weeks before decompensation. Key challenges: multi-disease validation, clinical workflow integration, equitable multilingual deployment. Success would transform episodic neurology into continuous personalized monitoring for millions globally.