Luo Junyu, Qian Cheng, Wang Xiaochen, Glass Lucas, Ma Fenglong
The Pennsylvania State University, University Park, USA.
IQVIA, Chicago, USA.
Proc ACM Int Conf Inf Knowl Manag. 2023 Oct;2023:4724-4730. doi: 10.1145/3583780.3615490. Epub 2023 Oct 21.
Predicting adverse drug reactions (ADRs) of drugs is one of the most critical steps in drug development. By pre-estimating the adverse reactions, researchers and drug development companies can greatly prevent the potential ADR risks and tragedies. However, the current ADR prediction methods suffer from several limitations. First, the prediction results are based on pure drug-related information, which makes them impossible to be directly applied for the personalized ADR prediction task. The lack of personalization of models also makes rare adverse events hard to be predicted. Therefore, it is of great interest to develop a new personalized ADR prediction method by introducing additional sources, e.g., patient health records. However, few methods have tried to use additional sources. In the meantime, the variety of different source formats and structures makes this task more challenging. To address the above challenges, we propose a novel personalized multi-sourced-based drug adverse reaction prediction model named pADR. pADR first works on every single source to transform them into proper representations. Next, a hierarchical multi-sourced Transformer is designed to automatically model the interactions between different sources and fuse them together for the final adverse event prediction. Experimental results on a new multi-sourced ADR prediction dataset show that PADR outperforms state-of-the-art drug-based baselines. Moreover, the case and ablation studies also illustrate the effectiveness of our proposed fusion strategies and the reasonableness of each module design.
预测药物的不良反应（ADR）是药物研发中最关键的步骤之一。通过预先估计不良反应，研究人员和药物研发公司可以极大地预防潜在的ADR风险和悲剧。然而，当前的ADR预测方法存在若干局限性。首先，预测结果基于纯粹的药物相关信息，这使得它们无法直接应用于个性化的ADR预测任务。模型缺乏个性化也使得罕见的不良事件难以预测。因此，通过引入额外的数据源（例如患者健康记录）来开发一种新的个性化ADR预测方法具有很大的吸引力。然而，很少有方法尝试使用额外的数据源。与此同时，不同源格式和结构的多样性使得这项任务更具挑战性。为了应对上述挑战，我们提出了一种名为pADR的基于多源的新型个性化药物不良反应预测模型。pADR首先对每个单独的源进行处理，将它们转换为适当的表示形式。接下来，设计了一个分层多源Transformer来自动建模不同源之间的相互作用，并将它们融合在一起以进行最终的不良事件预测。在一个新的多源ADR预测数据集上的实验结果表明，PADR优于基于药物的现有基准模型。此外，案例和消融研究也说明了我们提出的融合策略的有效性以及每个模块设计的合理性。
