  Patients with Acute Kidney Injury (AKI) increase mortality, morbidity, and
long-term adverse events. Therefore, early identification of AKI may improve
renal function recovery, decrease comorbidities, and further improve patients'
survival. To control certain risk factors and develop targeted prevention
strategies are important to reduce the risk of AKI. Drug-drug interactions and
drug-disease interactions are critical issues for AKI. Typical statistical
approaches cannot handle the complexity of drug-drug and drug-disease
interactions. In this paper, we propose a novel learning algorithm, Deep Rule
Forests (DRF), which discovers rules from multilayer tree models as the
combinations of drug usages and disease indications to help identify such
interactions. We found that several disease and drug usages are considered
having significant impact on the occurrence of AKI. Our experimental results
also show that the DRF model performs comparatively better than typical
tree-based and other state-of-the-art algorithms in terms of prediction
accuracy and model interpretability.
