Safe drug recommendation through forward data imputation and recurrent residual neural network

Abstract

Drug recommendation is crucial for aiding clinical decisions and promoting the scientific and rational use of drugs. The goal of drug recommendation is to provide safe and effective drug combinations for patients based on their historical medical records. Previous research heavily relies on electronic health records (EHRs) for data analysis. However, incomplete information and the presence of abnormal data in EHRs often lead to imprecise patient characterization. And it is challenging to learn patient representations and efficiently recommend safe drug combinations through EHRs. To tackle these concerns, this paper presents FDIRNet model to improve the performance of drug recommendation. FDIRNet employs forward data imputation to deal with missing and abnormal data of single-visit and multi-visits. Meanwhile, FDIRNet utilizes recurrent neural networks and residual neural networks to capture the changing patterns of a patient’s historical information. In addition, a novel loss function is proposed, which is combined with a Drug-Drug Interaction (DDI) loss strategy to mitigate DDI, thereby improving the overall robustness of the model. FDIRNet is evaluated on the publicly available dataset and it exhibits significant improvements over previous methods. We have observed distinct increases of 3.1%, 1.6%, and 3.1% in Jaccard, F1 score, and PRAUC, respectively.