发表机构
Saarland University; German Research Center for Artificial Intelligence (DFKI)（萨尔大学; 德国人工智能研究中心（DFKI））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CAMOS，一种耦合振荡状态空间模型，通过可用性门控的耦合矩阵处理多模态临床时间序列中的缺失模态，在ADNI和OASIS-3上优于现有方法。
AI 中文摘要
纵向临床队列是多模态、不规则采样且普遍不完整的：在ADNI中，正电子发射断层扫描和脑脊液检测在约一半的访问中缺失。线性状态空间模型能够优雅地处理不规则采样，但通过掩蔽输入来处理缺失模态，使转移算子保持不变。我们证明这是一个表示局限性：任何转移算子不依赖于可用性模式的线性状态空间层的潜在状态是可用性指标的加性函数，因此没有这样的层能够表示两种模态同时存在或同时缺失时的交互作用。我们提出CAMOS，它为每种模态提供一组二阶振荡器，通过一个位于微分方程内部且由可用性门控的矩阵进行耦合，从而使转移算子本身成为所采取测量的函数。耦合使得非耦合振荡模型的分析失效，我们恢复了它：每通道的Gershgorin预算使有效刚度在所有$2^M$个可用性模式和所有间隙上一致正定，能量论证将放大归因于可用性转换而非序列长度，通道分解保留了精确的关联并行扫描。在ADNI上，CAMOS在同访次分期、里程碑预测和纵向预测方面优于非耦合振荡状态空间模型和临床融合模型，并且在零样本迁移到OASIS-3时，它是唯一避免坍缩到多数类的模型。
英文摘要
Longitudinal clinical cohorts are multimodal, irregularly sampled and pervasively incomplete: in ADNI, positron emission tomography and cerebrospinal fluid assays are absent from roughly half of all visits. Linear state-space models handle irregular sampling gracefully but treat a missing modality by masking the input, leaving the transition operator untouched. We prove that this is a representational limitation: the latent state of any linear state-space layer whose transition operator does not depend on the availability pattern is an additive function of the availability indicators, so no such layer can represent an interaction between two modalities being jointly present or jointly absent. We propose CAMOS, which gives each modality a bank of second-order oscillators coupled through a matrix that sits inside the differential equation and is gated by availability, so the transition operator itself becomes a function of which measurements were taken. Coupling invalidates the analysis of uncoupled oscillatory models, and we restore it: a per-channel Gershgorin budget makes the effective stiffness positive definite uniformly over all $2^M$ availability patterns and all gaps, an energy argument charges amplification to availability transitions rather than sequence length, and a channel factorization preserves exact associative parallel scans. On ADNI, CAMOS outperforms uncoupled oscillatory state-space models and clinical fusion models on same-visit staging, landmark prediction and longitudinal forecasting, and under zero-shot transfer to OASIS-3 it is the only model that avoids collapse to the majority class.
