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Acute Kidney Injury (AKI), the abrupt decline in kidney function due totemporary or permanent injury, is associated with increased mortality,morbidity, length of stay, and hospital cost. Sometimes, simple interventionssuch as medication review or hydration can prevent AKI. There is thereforeinterest in estimating risk of AKI at hospitalization. To gain insight intothis task, we employ multilayer perceptron (MLP) and recurrent neural networks(RNNs) using serum creatinine (sCr) as a lone feature. We explore differentfeature input structures, including variable-length look-backs and a nestedformulation for rehospitalized patients with previous sCr measurements.Experimental results show that the simplest model, MLP processing the sum ofsCr, had best performance: AUROC 0.92 and AUPRC 0.70. Such a simple model couldbe easily integrated into an EHR. Preliminary results also suggest thatinpatient data streams with missing outpatient measurements---common in themedical setting---might be best modeled with a tailored architecture.

Sum of previous inpatient serum creatinine measurements predicts acute kidney injury in rehospitalized patients