Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts

Predicting how long a patient will survive — and what risks they face — is one of medicine's most consequential tasks, yet most deep learning survival models treat all patients with a single shared representation that can obscure critical subgroup differences. AdaCSM addresses this with a Mixture-of-Experts framework that dynamically routes patients to specialized risk predictors while simultaneously clustering them into meaningful subtypes. Tested across multiple real-world clinical cohorts spanning diverse diseases, it outperforms state-of-the-art baselines while producing interpretable risk stratification. Applications include oncology treatment planning, chronic disease management, clinical trial patient selection, and any setting where understanding why one patient group differs from another is as important as the prediction itself. Authors: Farica Zhuang, Zixuan Wen, Christos Davatzikos, Li Shen Paper: https://arxiv.org/abs/2606.14608v1