A Data Mining Approach for Predicting Delirium After Cardiac Surgery

Abstract

Of particular concern to patients is the effect of surgery upon brain functions following a surgical intervention. Indeed, post-operative neurocognitive complications occur in up to 60% of patients. Delirium is a temporary disturbance of consciousness, attention, cognition, and/or perception, which occurs frequently among hospitalized patients. It develops over a short period and tends to fluctuate. Several risk factors predispose patients to post-operative delirium. Patients who experience delirium after cardiac surgery are at higher risk of multiple adverse outcomes (e.g.: infections). Identification of patients at risk will allow targeted personalized preventive strategies. This thesis demonstrates the development of several predictive models, using a data mining approach, to predict the development of delirium in patients undergoing cardiac surgery. The findings of this research suggest that, applying machine learning and data mining techniques on complex and imbalanced medical data is capable of achieving superior results in comparison to standard statistical approaches.