A Reproducible ETL Approach for Window-based Prediction of Acute Kidney Injury in Critical Care Unit and Some Preliminary Results with Support Vector Machines

Acute kidney injury (AKI) is a frequent complication in hospitalized patients, and is associated with worse short and long-term outcomes. An early prediction of AKI to detect the patients at risk could be a first step in the discovery and assessment of new therapies, and in improvements of patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed the development of predictive models of AKI diagnosis. In this research work we provide a consistent reproducible ETL pipeline for Intensive Care Unit (ICU) data, in particular regarding the MIMIC III database, to support the early prediction of AKI. Then, we build different predictive models aimed at early identifying subjects who could experience AKI syndrome in their next 7 days after the ICU admission. The entire procedure is based on a recently proposed rolling observational window approach. We consider two predictive models, Gradient Boosting Decision Trees and Support Vector Machines, via different platforms.