Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests

TL;DR

This paper introduces Deep Rule Forests, a novel deep learning method that effectively identifies complex drug-drug and drug-disease interactions associated with Acute Kidney Injury, outperforming existing models in accuracy and interpretability.

Contribution

The paper presents Deep Rule Forests, a new algorithm that discovers interpretable rules from multilayer tree models for predicting AKI related interactions.

Findings

DRF outperforms traditional tree-based models in accuracy.

Several drugs and diseases significantly impact AKI occurrence.

DRF provides better interpretability for clinical decision-making.

Abstract

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.