MultSurv: A Multimodal Deep Learning Model for Hospitalized Patients Survival Analysis in the Context of a Pandemic - Zeiser, Felipe André; Costa, Cristiano André da; Ramos, Gabriel de Oliveira (Companion Proceedings of the Brazilian Symposium on Computing Applied to Healthcare (SBCAS); 2025: Companion Proceedings of the 25th Brazilian Symposium on Computing Applied to Health, 2025)
Respiratory infectious diseases continue to be a significant public health challenge. For example, the COVID-19 pandemic has exposed critical weaknesses in healthcare systems worldwide. The heterogeneity of clinical manifestations in hospitalized patients highlights the need for risk stratification methods to aid in better patient management and resource allocation. In this study, we propose MultSurv MultSurv, a multimodal survival analysis model that integrates clinical, laboratory, and chest X-ray data to capture the temporal dynamics of disease progression. The methodology comprises five main components: pre-processing, feature encoders, temporal attention, CheXReport for image analysis, and multitask neural networks for risk assessment. The model was evaluated using a private dataset, demonstrating superior performance over state-of-the-art methods, with a C-index of 0.723(t = 1) and 0.695(t = 7). The proposed approach can improve patient prioritization in pandemic scenarios and be adapted for broader clinical applications, potentially contributing to better decision-making in healthcare settings.