ADRNet: A Generalized Collaborative Filtering Framework Combining
  Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Haoxuan Li Taojun Hu Zetong Xiong Chunyuan Zheng Fuli Feng Xiangnan He Xiao-Hua Zhou
Adverse drug reaction (ADR) prediction plays a crucial role in both health
care and drug discovery for reducing patient mortality and enhancing drug
safety. Recently, many studies have been devoted to effectively predict the
drug-ADRs incidence rates. However, these methods either did not effectively
utilize non-clinical data, i.e., physical, chemical, and biological information
about the drug, or did little to establish a link between content-based and
pure collaborative filtering during the training phase. In this paper, we first
formulate the prediction of multi-label ADRs as a drug-ADR collaborative
filtering problem, and to the best of our knowledge, this is the first work to
provide extensive benchmark results of previous collaborative filtering methods
on two large publicly available clinical datasets. Then, by exploiting the easy
accessible drug characteristics from non-clinical data, we propose ADRNet, a
generalized collaborative filtering framework combining clinical and
non-clinical data for drug-ADR prediction. Specifically, ADRNet has a shallow
collaborative filtering module and a deep drug representation module, which can
exploit the high-dimensional drug descriptors to further guide the learning of
low-dimensional ADR latent embeddings, which incorporates both the benefits of
collaborative filtering and representation learning. Extensive experiments are
conducted on two publicly available real-world drug-ADR clinical datasets and
two non-clinical datasets to demonstrate the accuracy and efficiency of the
proposed ADRNet. The code is available at
https://github.com/haoxuanli-pku/ADRnet.