Multi-outcome predictive modelling of anesthesia patients

Abstract

Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth, and cardiorespiratory parameters which can also be altered by patient conditions and surgical procedures. Using artificial intelligence technology to continuously gather data of drug infusion and patient outcomes, we can generate reliable computer models individualized for a patient during specific stages of particular surgical procedures. This data can then be used to extend the current anesthesia monitoring functions to include future impact prediction, drug administration planning, and anesthesia decisions.