Predictive estimations of health systems resilience using machine learning - Alessandro Jatobá; Paula de Castro-Nunes; Paloma Palmieri; Omara Machado Araujo de Oliveira; Patricia Passos Simões; Valéria da Silva Fonseca; Paulo Victor Rodrigues de Carvalho (BMC Medical Informatics and Decision Making)
Abstract Operationalizing resilience in public health systems is critical for enhancing adaptive capacity during crises. This study presents a Machine Learning (ML) -based approach to assess resilience of the health system. Using historical data from Brazilian capitals, based on the World Health Organization’s six dimensions of resilient health systems, the study aims to predict responses of the system to stressors. A comprehensive dataset was developed through rigorous data collection and preprocessing, followed by splitting the data into training and testing subsets. Various ML algorithms, including regression models and decision trees, were applied to uncover insights into the resilience of health systems over time. Results revealed significant correlations between key indicators—such as outpatient care and availability of healthcare workforce—and the system’s resilience. It was shown that expanding these capacities enhances overall resilience. This research highlights the potential of ML in predictive modeling to inform strategic health decision-making, targeting interventions and more effective resource allocation. This study provides a robust framework for evaluating resilience, offering public health managers a valuable tool to strengthen health systems in the face of emerging challenges.