Desarrollo de algoritmos de Machine Learning para anestesia


Vilana García-Ovies, Ana


In this work, a first introduction is made to the world of anesthesia, to the parameters that are required to control the patient's condition and to the technical equipment necessary to monitor all the data and to be able to continuously monitor all the variables. that influence during the sedation of the patient.
It is extremely important to have all the parameters under control, but special attention is paid to the Burst Suppression pattern and the hypotension phenomenon, that is, the presence of blood pressure much lower than normal. Although there are various factors that can affect the patient's health and consequently hinder the recovery process, numerous studies indicate that the two previously mentioned are the main cause of the patient suffering complications during the postoperative period. It is for this reason that the implementation of the project is based on these two study variables.
In order to improve the scope of anesthesia, it was decided to generate various algorithm-based prediction models, which allow determining whether a patient will present Burst Suppression and / or hypotension. The result is to determine which of the models is the most optimal for making such a prediction, in such a way that the medical team would be able to anticipate the problem, react to it and reduce the risk of the patient suffering possible complications in the postoperative period.



Fernández Esmerats, Joan


IQS SE - Undergraduate Program in Industrial Engineering