Adverse drug–drug interaction (ADDI) causes harmful injuries and accidental deaths in patients, posing as a significant life-threatening issue in public health. Early prediction of ADDIs has become an increasingly concerning task for the safety of pharmacotherapy during clinical treatments. In this article, we propose an adaptive regularized multiattribute fuzzy distance (MAFD) learning model for ADDI prediction. Unlike the existing works that only focus on whether an adverse interaction occurs or not for a specific drug pair and do not consider their implicit medication risks, MAFD employs fuzzy distance learning by designing a fuzzy membership matrix to model the adverse distance with a fuzziness level for exploring the medication risks of adverse drug pairs. Meanwhile, for each attribute, we develop two projection matrices to respectively map its original feature and adverse interaction spaces into a common space for eliminating noisy information and capturing their compact and informative representations. Besides, adaptive regularization is explicitly designed to investigate the underlying characteristics of different attributes in ADDI modeling and neighborhood structure preservation is seamlessly integrated to benefit the prediction results. The optimization problem is solved by an iterative algorithm based on the alternating direction method of multipliers with detailed convergence proofs. Experiments on real-world dataset demonstrate the effectiveness of MAFD when compared with ten baselines and its five variants.

中文翻译：

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用于预测不良药物相互作用的自适应正则化多属性模糊距离学习


药物不良相互作用（ADDI）会导致患者受到伤害和意外死亡，成为公共卫生中一个重大的危及生命的问题。 ADDI 的早期预测已成为临床治疗过程中药物治疗安全性日益令人担忧的任务。在本文中，我们提出了一种用于 ADDI 预测的自适应正则化多属性模糊距离（MAFD）学习模型。与现有的仅关注特定药物对是否发生不良相互作用而不考虑其隐含用药风险的工作不同，MAFD 通过设计模糊隶属度矩阵来采用模糊距离学习，以模糊水平对不良距离进行建模探讨不良药对的用药风险。同时，对于每个属性，我们开发了两个投影矩阵，分别将其原始特征和不利交互空间映射到公共空间中，以消除噪声信息并捕获其紧凑且信息丰富的表示。此外，自适应正则化被明确设计为研究ADDI建模中不同属性的潜在特征，并且邻域结构保留被无缝集成以有利于预测结果。优化问题通过基于乘子交替方向法的迭代算法来解决，并具有详细的收敛证明。真实世界数据集上的实验证明了 MAFD 与十个基线及其五个变体相比的有效性。