A novel pseudo-labeling framework based on matrix integration for adverse drug reaction prediction

Abstract

Background Adverse drug reactions (ADRs) are one of the global public health events that seriously endanger human life and cause high economic burdens. Therefore, predicting the possibility of their occurrence and taking early and effective response measures are of great significance. The construction of a correlation matrix between drugs and their adverse reactions, followed by effective correlation data mining, is one of the current strategies to predict ADRs using accessible public data. Since the number of known ADRs in real-world data is far less than the number of their unknown counterparts, the drug-ADR association matrix is very sparse, which greatly affects the classification performance of machine learning methods. Results To effectively solve the problem of sparsity, we propose a novel pseudo-labeled data augmentation framework that mines potential unknown drug-ADR pairs by integrating multiple weighted matrix factorization (MF) models and treating them as pseudo-labeled drug-ADR pairs. Pseudo-labeled data is added to the training set and the MF model is fine-tuned to improve the classification performance. Conclusions This paper reproduces the baselines under the same experimental conditions to evaluate the performance of the proposed method on sparse data from the Side Effect Resource (SIDER) database. Experimental results showed that our method outperformed other baselines in the Area Under Precision-Recall (AUPR) and F1 scores, and still maintained the best performance in sparser scenarios. Furthermore, we conducted a case study and the results showed that our proposed framework efficiently predicted ADRs in the real world.