Towards a pre-surgery clinical decision support system

Abstract

Recently, the field of anesthesiology has increasingly recognized the need for personalized medicine, aiming to tailor drug administration based on the unique physiological and clinical profiles of individual patients. This approach is particularly crucial in the administration of anaesthetic drugs, where inter-individual variability in response can significantly affect both the efficacy and safety of treatment. The main focus of this paper is on the relationship between the healthcare domain and innovative Machine Learning technologies. The specific case study analysed concerns the implementation of a predictive Bayesian Network (BN) model for the inductive administration of Propofol, an anaesthetic drug. The available data is taken from the PhysioNet platform and it relates to a study conducted on nine healthy volunteers who underwent drug administration for approximately three hours, measuring their vital parameters. The results indicate that the choice of the Bayesian Network (BN) formalism is highly suitable for the analysed case study. In conclusion, it is hypothesized that an analysis focused on patient-specific characteristics, such as gender, age, and medical history, could significantly improve the model’s accuracy.

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