论文标题
发现药物 - 药物和药物疾病相互作用，使用深度统治森林诱导急性肾脏损伤
Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests
论文作者
论文摘要
急性肾脏损伤（AKI）患者增加了死亡率，发病率和长期不良事件。因此，对AKI的早期鉴定可以改善肾功能恢复，减少合并症并进一步改善患者的生存率。控制某些风险因素并制定有针对性的预防策略对于降低AKI的风险很重要。药物 - 药物相互作用和药物疾病相互作用是AKI的关键问题。典型的统计方法无法处理药物 - 药物和药物疾病相互作用的复杂性。在本文中，我们提出了一种新颖的学习算法，深度统治森林（DRF），该算法从多层树模型中发现规则，作为药物使用和疾病适应症的组合，以帮助识别这种相互作用。我们发现，几种疾病和药物的使用被认为对AKI的发生有重大影响。我们的实验结果还表明，就预测准确性和模型的解释性而言，DRF模型的性能比典型的基于树和其他最先进的算法要好得多。
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.
