Modelo Dinâmico de Análise de Sobrevivência Aplicado à Previsão do Tempo de Internação em Hospitais Públicos Municipais

Abstract

Healthcare service overcrowding poses significant challenges to hospital management, with Length of Stay (LOS) being one of the main performance and planning indicators. In this context, this study proposes a dynamic survival analysis approach to predict hospital length of stay, incorporating temporal updating of risk estimates throughout the patient’s hospitalization. The proposed framework is evaluated using real-world data from the public hospital system of João Pessoa, Brazil, aiming to provide predictive estimates that better reflect the evolving nature of discharge risk over time. As part of the validation process, the proposed dynamic approach is assessed in comparison with well-established survival models, including the Cox Proportional Hazards model (CoxPH), Random Survival Forest, and DeepSurv. The study encompasses fundamental concepts of machine learning and deep learning, data preprocessing techniques, and survival analysis theory, structuring model development within a classical machine learning pipeline. The main outcome of this research is a predictive model capable of estimating conditional and dynamically updated probabilities associated with hospital length of stay, allowing recalibration as new clinical information becomes available. Model performance is evaluated using the concordance index (C-index), enabling comparison between the proposed dynamic framework and traditional approaches. Results indicate that dynamic modeling has the potential to enhance predictive accuracy and practical applicability, reinforcing the feasibility of artificial intelligence techniques in developing intelligent clinical decision-support systems and improving hospital bed management efficiency.