07/16/2026 | Press release | Distributed by Public on 07/16/2026 04:34
Abstract: Models that predict the survival and quality of life (QoL) in patients with malignancies can support prognostic counselling and patient-centered evaluation of disease trajectories. Challenges in the development of such models include incomplete data in real-world registries, including missing baseline measurements and irregular, sparse availability of longitudinal QoL outcomes during treatment. These limitations complicate the incorporation of QoL into survival prediction and the reliable modeling of QoL over time. We address these limitations with a novel framework to predict survival and quality of life in the face of sparse data.
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