CAMOS: Coupled Oscillatory State-Space Model for Multimodal Clinical Time-Series
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 abs...
| Authors | Maxx Richard Rahman, Mostafa Hammouda, Wolfgang Maass |
|---|---|
| Primary category | stat.ML |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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