Multi-agent model for risk prediction in surgery
This work addresses risk prediction in surgery for healthcare professionals, but it appears incremental as it builds on existing agent-based simulation methods and introduces case-based reasoning as a partial solution to identified challenges.
The paper tackles the problem of risk management in surgical procedures by implementing an agent-based simulation to generate alerts for various operating room settings, such as human fatigue and infection rates, and presents initial results while identifying scientific obstacles like integrating abstraction levels and deducing unpredictable alerts.
Risk management resulting from the actions and states of the different elements making up a operating room is a major concern during a surgical procedure. Agent-based simulation shows an interest through its interaction concepts, interactivity and autonomy of different simulator entities. We want in our study to implement a generator of alerts to listen the evolution of different settings applied to the simulator of agents (human fatigue, material efficiency, infection rate ...). This article presents our model, its implementation and the first results obtained. It should be noted that this study also made it possible to identify several scientific obstacles, such as the integration of different levels of abstraction, the coupling of species, the coexistence of several scales in the same environment and the deduction of unpredictable alerts. Case-based reasoning (CBR) is a beginning of response relative to the last lock mentioned and will be discussed in this paper.