Toward Continuous Neurocognitive Monitoring: Integrating Speech AI with Relational Graph Transformers for Rare Neurological Diseases

TL;DR

This paper introduces a novel approach combining speech AI and relational graph transformers for continuous monitoring of neurological diseases, demonstrated in PKU, aiming to detect cognitive decline earlier than traditional tests.

Contribution

It proposes integrating speech analysis with Relational Graph Transformers for real-time neurocognitive monitoring, addressing data heterogeneity and early detection challenges.

Findings

Speech-derived 'Proficiency in Verbal Discourse' correlates with blood phenylalanine levels.

RELGT can process heterogeneous medical data for predictive alerts.

Potential to enable continuous personalized neurological monitoring.

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 < 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.