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.

翻译：罕见神经系统疾病患者常报告认知症状——即“脑雾”——这些症状在传统测试中难以捕捉。我们提出通过智能手机语音分析结合关系图Transformer（RELGT）架构实现连续神经认知监测。在苯丙酮尿症（PKU）中的概念验证表明，基于语音衍生的“言语流畅性”与血液苯丙氨酸水平相关（p = -0.50，p < 0.005），但与标准认知测试无显著关联（所有|r| < 0.35）。RELGT有望克服异质性医疗数据（语音、实验室指标、评估结果）中的信息瓶颈，在失代偿发生前数周实现预测性预警。关键挑战包括：多疾病验证、临床工作流程整合、公平的多语言部署。若成功，该技术将推动全球数百万患者的神经疾病诊疗从间断性评估转向连续个性化监测。