Pediatric crisis risk
Development and evaluation of data mining algorithms for predicting crisis risk in outpatients of a pediatric hospital FONDEF Idea-CONICT (2014-2016)
Role: Technical Researcher, developer. Main director: Sebastian Ríos, University of Chile. Grant: FONDEF Idea-CONICT (2014-2016). CA13i-10300.
In this work, we hypothesize whether data mining techniques can help predict a potential respiratory crisis risk state in chronic respiratory pediatric patients. Our approach first processes use biometric features (temperature, heart rate, respiratory rate and oxygen saturation) based on Pediatric Advanced Life Support (PALS) and the type of respiratory support required by the patient. After three years of work, we developed a model which can predict a state we call respiratory crisis risk. To build and test the approach mentioned above, we leverage data of patients in the largest public pediatric hospital in Santiago, Chile.
More info at monitoreo-pediatrico-a-distancia
