Data mining and modeling to predict the necessity of vasopressors for ICU patients

Shock is a life-threatening medical condition requiring the administration of powerful drugs - vasopressors. Early identification of these patients is a worthy goal in order to timely prepare them for therapy. A subset composed of the most frequently sampled and readily available variables in an intensive care unit (ICU) was used for clustering patients. Then, a data exploration process was started through the use of fuzzy clustering with the fuzzy cmeans algorithm, where four clusters were obtained and the groups characteristics were analyzed. A relationship between the clusters obtained and the use of vasopressors was found out and these results were visualized with the help of histograms. First, a single model was derived. Then, four models were trained and used for a multi model approach, one for each identified group of patients. In both cases fuzzy models were used as they are universal approximators. For the multi-model approach, two decision criteria were used. First a decision a priori based on the distance from the clusters centers to the patient characteristics was used. Lastly a decision a posteriori approach where each model was used and the final outcome used is based on the uncertainty of the output response to the threshold of each model. The multi model approach with a posteriori decision had a better performance of the two schemes tested, and also performed better than the single general model approach.