SurvBridge: Survival Representation Transfer Across Heterogeneous Clinical Cohorts

Heterogeneous clinical cohorts often differ in available variables, observation processes, and follow-up, complicating the transfer of survival representations learned from large electronic health records (EHRs). We introduce SurvBridge, a schema-flexible framework for cross-cohort survival representation transfer that combines feature-semantic encoding, survival-aware multi-source contrastive pre-training, explicit representation of missingness state, and Cox-based survival adaptation. SurvBridge supports pre-training across multiple EHR sources with and without time-to-event labels, without requiring identical feature spaces, and adapts the learned representation to target-native clinical schemas. We evaluate SurvBridge on three public clinical survival datasets and two independent cardiovascular cohorts. SurvBridge achieves strong survival discrimination against classical, deep survival, and tabular foundation-model baselines. Pre-training provides its largest gains when labeled target data are scarce, with SurvVPCL providing more consistent low-resource transfer than the alternative survival-aware contrastive objectives evaluated. On naturally incomplete clinical data, explicitly representing missingness state improves mean discrimination over deterministic missing-value handling and changes how partially observed clinical variables contribute to predicted risk. These findings position cross-cohort survival pre-training primarily as a means of improving sample efficiency while accommodating heterogeneous clinical feature spaces and missingness representation.