发表机构
University of Nevada, Reno; Connected Future Labs（内华达大学雷诺分校; 互联未来实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于Transformer跨模态融合的BioSync模型，构建BSI数字生物标志物，在两个合成队列实验中优于拼接等方法，相关系数较高，计算特性明确，需真实队列验证。
AI 中文摘要
来自可穿戴设备和移动设备的心脏、神经、行为及语音测量数据，提供了对生理状态的部分、易受噪声影响的观测视角。BioSync将这些测量数据整合为BEST框架下定义的连续复合数字生物标志物——BioSync指数（BioSync Index, BSI）。该模型对模态令牌应用多头自注意力机制，并添加了一个线性分支，其假设类包含标准特征拼接。这一架构的提出基于潜变量测量理论，以及联合观测可能包含单一模态无法提供的信息的可能性。我们在两个文献驱动的合成队列上评估了BioSync：一个使用心率变异性（HRV）、脑电图（EEG）、活动记录仪和语音的四模态认知衰退队列，另一个基于公开AI-READI可穿戴方案构建的代谢自主神经队列。在认知队列中，BioSync和拼接方法的AUC分别为0.928和0.926；在代谢队列中，BioSync的准确率/F1值为0.764/0.766，而拼接方法为0.756/0.758。BSI在两个队列中均与潜变量严重程度相关（相关系数r=0.91和r=0.68）。纯注意力机制的消融实验得到认知队列AUC为0.911，表明0.928的提升来自宽深结合的架构。在匹配模态失活训练下，BioSync在认知队列的6种损坏率中的5种，以及代谢队列的最高损坏率上优于拼接方法。其认知队列AUC也高于5个已发表的数字生物标志物参考值，但由于数据集和任务的差异，无法得出受控基准结论。通过与单模态、早期融合和晚期融合设计在6项预定义标准上的比较，明确了该模型的计算特性，其在真实队列上的验证仍有待开展。
英文摘要
Cardiac, neural, behavioral, and speech measurements from wearable and mobile devices provide partial, noise-sensitive views of physiological state. BioSync combines these measurements into the \textbf{BioSync Index (BSI)}, a continuous composite digital biomarker defined under the BEST framework. The model applies multi-head self-attention to modality tokens and adds a linear branch whose hypothesis class includes standard feature concatenation. This architecture is motivated by latent-variable measurement theory and by the possibility that joint observations contain information unavailable from individual modalities. We evaluated BioSync on two literature-informed synthetic cohorts: a four-modality cognitive-decline cohort using HRV, EEG, actigraphy, and speech, and a metabolic-autonomic cohort structured around the public AI-READI wearable schema. In the cognitive cohort, BioSync and concatenation obtained AUCs of 0.928 and 0.926, respectively. In the metabolic cohort, BioSync obtained accuracy/F1 of 0.764/0.766, compared with 0.756/0.758 for concatenation. The BSI correlated with latent severity in both cohorts ($r=0.91$ and $r=0.68$). A pure-attention ablation obtained cognitive-cohort AUC 0.911, locating the increase to 0.928 in the combined wide-and-deep architecture. With matched modality-dropout training, BioSync led concatenation at five of six cognitive-cohort corruption rates and at the highest metabolic-cohort rate. Its cognitive-cohort AUC was also higher than five published digital-biomarker reference values, although differences in datasets and tasks preclude a controlled benchmark claim. Comparison with single-modality, early-fusion, and late-fusion designs across six prespecified criteria identifies the model's computational properties; validation on real cohorts remains necessary.
