Fuzzy modeling to predict short and long-term mortality among patients with Acute Kidney Injury

Abstract

Acute kidney injury (AKI) affects 5 to 7% of all hospitalized patients, with a much higher incidence in the critically ill. Although AKI patients have increased risks of death in the intensive care units (ICU) and in the short term, few studies examined the association between the diagnosis of AKI and long-term outcomes. The aim of this study is to address the prediction of short and long-term mortality in patients that presented AKI diagnosis at their hospital admission. Demographic, clinical, physiologic, and date of death data of ICU patients were extracted from the MIMIC-III database to develop predictive models. Fuzzy models are developed using fuzzy c-means and Gustafson-Kessel algorithms to predict mortality in the ICU within 24 hours, when the patients had an ICD-9 admission diagnosis of AKI. The proposed models achieved an AUC of 0.77, which is slightly better than the results obtained by Celi et al. [1] that obtained an AUC of 0.74. Thus, AKI revealed to be a significant risk factor for in-hospital and long-term mortality for ICU patients.