Supporting the Prediction of AKI Evolution Through Interval-Based Approximate Predictive Functional Dependencies

In this paper, we focus on the early prediction of patterns related to the severity stage of Acute Kidney Injury (AKI) in an ICU setting. Such problem is challenging from several points of view: (i) AKI in ICU is a high-risk complication for ICU patients and needs to be suitably prevented, and (ii) the detection of AKI pathological states is done with some delay, due to the required data collection. To support the early prediction of AKI diagnosis, we extend a recently-proposed temporal framework to deal with the prediction of multivalued interval-based patterns, representing the evolution of pathological states of patients. We evaluated our approach on the MIMIC-IV dataset.