1 research outputs found
Sum of previous inpatient serum creatinine measurements predicts acute kidney injury in rehospitalized patients
Acute Kidney Injury (AKI), the abrupt decline in kidney function due to
temporary or permanent injury, is associated with increased mortality,
morbidity, length of stay, and hospital cost. Sometimes, simple interventions
such as medication review or hydration can prevent AKI. There is therefore
interest in estimating risk of AKI at hospitalization. To gain insight into
this task, we employ multilayer perceptron (MLP) and recurrent neural networks
(RNNs) using serum creatinine (sCr) as a lone feature. We explore different
feature input structures, including variable-length look-backs and a nested
formulation for rehospitalized patients with previous sCr measurements.
Experimental results show that the simplest model, MLP processing the sum of
sCr, had best performance: AUROC 0.92 and AUPRC 0.70. Such a simple model could
be easily integrated into an EHR. Preliminary results also suggest that
inpatient data streams with missing outpatient measurements---common in the
medical setting---might be best modeled with a tailored architecture.Comment: Accepted poster at NIPS 2017 Workshop on Machine Learning for Health
(https://ml4health.github.io/2017/