DISCERN: Identifiable Disease Evidence for Generalizable Medical Multimodal Learning

Multimodal clinical models are trained on cohorts that differ in whom they enroll and how they measure. Enrollment that ties background factors such as age to the disease state leaks into every modality, so models learn shortcuts that fail on new cohorts. We model medical multimodal data as a latent structural causal model in which a latent disease state drives, and in a second regime rewires, a graph of latent factors; each modality and the recorded endpoint are partial views of these factors, and cohorts differ only in selection and measurement. We show that the disease state need not be observed and the full model need not be recovered: in common Gaussian coordinates, states can be aligned across cohorts, enrollment-induced changes identify a minimal selection statistic, and conditioning on it yields disease evidence that is identifiable without state labels and invariant to enrollment shifts within the identified family. When the disease state rewires the factors, this evidence can be invisible to each modality and to late fusion of their posteriors, yet recoverable from the modalities jointly. DISCERN instantiates the theory in a jointly trained encoder, latent-state model and outcome head. Under reversed synthetic enrollment, conditioning raises state AUROC from 0.043 to 0.883 and from 0.265 to 0.825, while random conditioning does not; on cross-database mortality prediction between eICU and MIMIC-IV, DISCERN improves AUROC over matched empirical risk minimization by 0.036 and 0.035.