A continual prediction model for inpatient acute kidney injury

Abstract

Acute kidney injury (AKI) commonly occurs in hospitalized patients and can lead to serious medical complications. But it is preventable and potentially reversible with early diagnosis and management. Therefore, several machine learning based predictive models have been built to predict AKI in advance from electronic health records (EHR) data. These models to predict inpatient AKI were always built to make predictions at a particular time, for example, 24 or 48 h from admission. However, hospital stays can be several days long and AKI can develop any time within a few hours. To optimally predict AKI before it develops at any time during a hospital stay, we present a novel framework in which AKI is continually predicted automatically from EHR data over the entire hospital stay. The continual model predicts AKI every time a patient's AKI-relevant variable changes in the EHR. Thus, the model not only is independent of a particular time for making predictions, it can also leverage the latest values of all the AKI-relevant patient variables for making predictions. A method to comprehensively evaluate the overall performance of a continual prediction model is also introduced, and we experimentally show using a large dataset of hospital stays that the continual prediction model out-performs all one-time prediction models in predicting AKI.

Keywords: Acute kidney injury; EHR; Machine learning; Prediction.