Daily prediction of ICU readmissions using feature engineering and ensemble fuzzy modeling

This research is focused on the prediction of ICU readmissions using fuzzy modeling and feature selection approaches. There are a number of published scores for assessing the risk of readmissions, but their poor predictive performance renders them unsuitable for implementation in the clinical setting. In this work, we propose the use of feature engineering and advanced computational intelligence techniques to improve the performance of current models. In particular, we propose an approach that relies on transforming raw vital signs, laboratory results and demographic information into more informative pieces of data, selecting a subset of relevant and non-redundant variables and applying fuzzy ensemble modeling to the feature-engineered data for deriving important nonlinear relations between variables. Different criteria for selecting the best predictor from the ensemble and novel evaluation measures are explored. In particular, the area under the sensitivity curve and area under the specificity curve are investigated. The ensemble approach combined with feature transformation and feature selection showed increased performance, being able to predict early readmissions with an AUC of 0.77 ± 0.02. To the best of our knowledge, this is the first computational intelligence technique allowing the prediction of readmissions in a daily basis. The high balance between sensitivity and specificity shows its strength and suitability for the management of the patient discharge decision making process.