Title: Stochastic block modelling and link prediction to improve mortality prediction for critical patients.
Speaker: Jorge Gaete Villegas
Mortality prediction for patients in Intensive Care Units (ICU) is an important but challenging task. Early prediction can improve medical outcomes, optimize medical interventions, and minimize the use of resources. Current efforts to create mortality prediction models rely on medical consensus, regression methods, and machine learning. Unfortunately, the nature and quality of ICU data can affect the performance of such models. Some of the shortcomings reported in the literature include the overestimation of mortality for older patients and low predictive power for underrepresented patient groups. In this talk we present our current work exploring stochastic block modelling and link prediction to forecast mortality and overcome such shortcomings.