Risk Prediction in Surgery Using Case-Based Reasoning and Agent-Based Modelization

Managing the risks arising from the actions and conditions of the various elements that make up an operating room is a major concern during a surgical procedure. One of the main challenges is to define alert thresholds in a non-deterministic context where unpredictable adverse events occur. In response to this problematic, this paper presents an architecture that couples a Multi-Agent System (MAS) with Case-Based Reasoning (CBR). The possibility of emulating a large number of situations thanks to MAS, combined with analytical data management thanks to CBR, is an original and efficient way of determining thresholds that are not defined a priori. We also compared different similarity calculation methods (Retrieve phase of CBR). The results presented in this article show that our model can manage alert thresholds in an environment that manages data as disparate as infectious agents, patient's vitals and human fatigue. In addition, they reveal that the thresholds proposed by the system are more efficient than the predefined ones. These results tend to prove that our simulator is an effective alert generator. Nevertheless, the context remains a simulation mode that we would like to enrich with real data from, for example, monitoring sensors (bracelet for human fatigue, monitoring, etc).