Fuzzy modeling to predict administration of vasopressors in intensive care unit patients

Vasopressors belong to a powerful class of drugs used in the management of systemic shock in ill patients. The administration of a vasopressor involves the non-trivial process of inserting a central venous catheter. This procedure carries with it inherent risks which are increased when done under urgency such as in the case of unexpected systemic shock. The ability to predict the transition to vasopressor dependence could be expected to improve overall outcomes associated with the procedure. We use three different approaches combining fuzzy modeling with bottom-up (BU), top-town (TD) and ant feature selection (AFS), to classify requirements for vasopressors in shock. We observe that fuzzy models combined with BU feature selection return higher values of sensitivity; fuzzy models with no feature selection and fuzzy models with TD feature selection return higher values of AUC and specificity; features most commonly selected to classify impending use of vasopressores in pancreatitis patients include levels of Sodium and White Blood Cell counts, while for pneumonia patients include levels of Lactid Acid and White Blood Cell Count; and finally, fuzzy models combined with BU and fuzzy models combined with AFS demonstrate the lowest number of selected variables with no significant loss in accuracy.