SADEBE 2.0: programming structure for decision support systems in health - Fernandes, Marciele de Lima; Chaves, Elliott Victor de Sousa; Albuquerque, Sarah Loyse Silva Cavalcanti de; Bezerra, Raphaely Domingues; Sales, Alberlene Baracho (Brazilian Journal of Production Engineering, 2026)
SADEBE 2.0 features an enhanced programming framework designed for the development of Health Decision Support Systems, with an emphasis on standardization, integration, and efficiency in clinical data processing. Compared to the original version, it has incorporated new modules, expanded interoperability, and introduced advanced analysis mechanisms, strengthening support for evidence-based decision-making by professionals and managers. In addition, the system now employs two decision support methods Random Forest and Hidden Naive Bayes enhancing the robustness and accuracy of clinical recommendations. Based on validated clinical rules, SADEBE 2.0 enables the construction of a clear, objective, and reliable decision-making flow, guiding nursing practices in a systematic and safe manner. The implementation in PHP contributed to an intuitive interface, ensuring efficient processing of clinical rules and integration with a database for storing evidence and care records. In this way, the system strengthens professional nursing practice, promoting greater precision, agility, and safety in patient care.