{"ID":2833064,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04938","arxiv_id":"2512.04938","title":"Toward Continuous Neurocognitive Monitoring: Integrating Speech AI with Relational Graph Transformers for Rare Neurological Diseases","abstract":"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 \u003c 0.005) but not standard cognitive tests (all |r| \u003c 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.","short_abstract":"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-deriv...","url_abs":"https://arxiv.org/abs/2512.04938","url_pdf":"https://arxiv.org/pdf/2512.04938v1","authors":"[\"Raquel Norel\",\"Michele Merler\",\"Pavitra Modi\"]","published":"2025-12-04T16:06:50Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}