用手机语音分析+图模型，持续监测罕见病认知衰退。
Toward Continuous Neurocognitive Monitoring: Integrating Speech AI with Relational Graph Transformers for Rare Neurological Diseases
- 结合语音分析与关系图网络，实现多源医疗数据融合
- 语音指标与苯丙氨酸水平相关性达-0.50（p<0.005）
- 适合罕见病患者长期追踪，推动个性化神经监测
罕见神经系统疾病患者常报告认知症状——“脑雾”，传统检测手段难以捕捉。本文提出通过智能手机语音分析结合关系图变换器（RELGT）架构，实现连续神经认知监测。在苯丙酮尿症（PKU）的初步验证中，语音衍生的“语言表达熟练度”与血液苯丙氨酸水平呈显著负相关（r = -0.50，p < 0.005），而标准认知测试相关性均低于0.35（|r| < 0.35）。RELGT可缓解异构医疗数据（语音、检验、评估）中的信息瓶颈，提前数周预测病情恶化。关键挑战包括多病种验证、临床流程整合及多语言公平部署。成功将推动全球数百万患者从间歇性神经科诊疗转向持续个性化监测。
原文摘要 · Abstract (English)
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.
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