DEMDE: Decision Making Design based on Bayesian Network for Personalized Monitoring System

Personalized monitoring systems (PMS) are used for Decision Making (DeM) to support humans and fully autonomous decision-making in several applications, such as health monitoring and management. However, it is still challenging to design decision-making in PMS. In this work, we propose a systematic modeling approach, called DEMDE, for decision-making design in PMS during the design phase. DEMDE guides the design of a specific Bayesian network (BN) from an instantiated domain model for PMS based on context-aware data fusion using a probabilistic domain model (general BN). We evaluated our proposal by developing a BN for decision-making about sending an alert of high contamination risk in cell culture. The case study demonstrated the application of the DEMDE concepts and the modeling process, including model evaluation through sensitivity analysis to assess the robustness and reliability of the modeled BN.