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

2025-12-04

AI Summary

Conventional cognitive assessments often fail to capture subtle, fluctuating neurocognitive impairments—such as “brain fog”—in rare neurological disorders like phenylketonuria (PKU). Method: We propose a smartphone-based continuous monitoring paradigm leveraging spontaneous speech, and introduce the Relation Graph Transformer (RELGT), a multimodal model integrating speech features, laboratory biomarkers (e.g., blood phenylalanine), and clinical metadata to derive a dynamic, quantifiable metric: “verbal expression proficiency.” Contribution/Results: This is the first work to synergize speech AI with heterogeneous medical knowledge graph modeling for prospective deterioration prediction—up to weeks before clinical decline. In a PKU cohort (n=42), verbal expression proficiency significantly correlated with blood phenylalanine levels (r = −0.50, p < 0.005) but showed no significant association with standard cognitive scales (|r| < 0.35), demonstrating superior sensitivity and translational potential. The framework advances neurology from episodic, static assessment toward personalized, longitudinal monitoring.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Continuous neurocognitive monitoring for rare neurological diseases using speech AI
• Overcoming information bottlenecks in heterogeneous medical data with Relational Graph Transformers
• Transforming episodic neurology into personalized, predictive monitoring via smartphone analysis

Innovation

• Smartphone speech analysis for continuous neurocognitive monitoring
• Relational Graph Transformers integrate heterogeneous medical data
• Predictive alerts weeks before clinical decompensation