Early Detection of Acute Kidney Injury with Bayesian Networks

Acute kidney injury (AKI) is a major health issue, affecting large numbers of patients worldwide. It is associated with an increase in complications and poor prognostics if diagnosis is delayed. Medical guidelines are routinely employed to classify different AKI stages, but guidance on the early detection of AKI risk is limited. In this paper, we present a Bayesian Networks (BN) proof of concept to predict the likelihood of AKI onset based on longitudinal patient data, such as serum creatinine values, demographics and comorbidities. Data for training and validating the model was obtained from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database. We describe the problem domain, data acquisition and preparation, model developed, results obtained and pertaining limitations. We demonstrate that our model can predict the onset of the disease with an accuracy of up to 87% (area under the curve of 0.87) in the cohort under analysis.