Machine Learning personalizationfor hypotension prediction

Abstract: Perioperative hypotension (PH), commonly a side effect of anesthesia,is one of the main mortality causes during the 30 posterior days of asurgical procedure. Novel research lines propose combining machinelearning algorithms with the Arterial Blood Pressure (ABP) waveform tonotify healthcare professionals about the onset of a hypotensive event withtime advance and prevent its occurrence. Nevertheless, ABP waveformsare heterogeneous among patients, consequently, a general model maypresent different predictive capabilities per individual. This project aimsat improving the performance of an artificial neural network (ANN) topredict hypotension events with time advance by applying personalizedmachine learning techniques, like data grouping and domain adaptation. Wehypothesize its implementation will allow us to cluster patients with similardemographic and ABP discriminative characteristics and tailor the modelto each specific group, resulting in a worst overall but better individualperformance. Results present a slight but not clinical significant improvementwhen comparing AUROC values between the group-specific and the generalmodel. This suggests even though personalization could be a good approach todealing with patient heterogeneity, the clustering algorithm presented in thisthesis is not sufficient to make the ANN clinically feasible.