  The incidence of Acute Kidney Injury (AKI) commonly happens in the Intensive
Care Unit (ICU) patients, especially in the adults, which is an independent
risk factor affecting short-term and long-term mortality. Though researchers in
recent years highlight the early prediction of AKI, the performance of existing
models are not precise enough. The objective of this research is to precisely
predict AKI by means of Convolutional Neural Network on Electronic Health
Record (EHR) data. The data sets used in this research are two public
Electronic Health Record (EHR) databases: MIMIC-III and eICU database. In this
study, we take several Convolutional Neural Network models to train and test
our AKI predictor, which can precisely predict whether a certain patient will
suffer from AKI after admission in ICU according to the last measurements of
the 16 blood gas and demographic features. The research is based on Kidney
Disease Improving Global Outcomes (KDIGO) criteria for AKI definition. Our work
greatly improves the AKI prediction precision, and the best AUROC is up to
0.988 on MIMIC-III data set and 0.936 on eICU data set, both of which
outperform the state-of-art predictors. And the dimension of the input vector
used in this predictor is much fewer than that used in other existing
researches. Compared with the existing AKI predictors, the predictor in this
work greatly improves the precision of early prediction of AKI by using the
Convolutional Neural Network architecture and a more concise input vector.
Early and precise prediction of AKI will bring much benefit to the decision of
treatment, so it is believed that our work is a very helpful clinical
application.
