MAS4GC: Multi-agent System for Glycemic Control of Intensive Care Unit Patients - Segato, Tiago Henrique Faccio; Serafim, Rafael Moura da Silva; Fernandes, Sérgio Eduardo Soares; Ralha, Célia Ghedini (Anais da Brazilian Conference on Intelligent Systems (BRACIS); 2021: Anais da X Brazilian Conference on Intelligent Systems, 2021)
The coronavirus disease (COVID-19) pandemic has brought significant challenges worldwide through the consequences of increasing demand for the Intensive Care Unit (ICU) resources. This work presents the Multi-Agent System for Glycemic Control (MAS4GC) to assist health professionals leading with critical patients in the ICU. More specifically, the MAS4GC manages patients’ blood glucose through glycemic predictions, treatment, and monitoring recommendations to health professionals. Prediction models are applied to monitor patients’ blood glucose allowing health professionals to carry out preventive treatments. The glycemic control is included in the FAST HUG mnemonic to remember the key issues in the supportive care of critically ill patients. The MAS4GC methodological development process is presented with Tropos modeling, architectural design, and implementation with the PADE framework. Agents’ inference mechanisms are based on production rules defined by intensive care physician specialists applying their knowledge to indicate treatments for patients. Two experiments using patients with synthetic data were conducted to evaluate the results of the MAS4GC: (1) the prediction model achieved 90% accuracy in blood glucose predictions for the next 4 h, (2) 84% similarity of treatment recommendations compared to a human specialist, and 78% in recommendations for monitoring glycemic of critical patients.