Improving the Prediction of Adverse Drug Events Using Feature Fusion-Based Predictive Network Models

Abstract

Computational strategies play a vital role in the prediction of adverse drug events (ADEs) owing to their low cost and increased efficiency. In this study, we used the strengths of the Jaccard and Adamic-Adar indices to build feature fusion-based predictive network models (FFPNMs) with three different machine learning (ML) methods respectively to predict drug-ADE associations. Our FFPNM with the logistic regression (LR) model improved to an area under the receiver operating characteristic curve (AUROC) value of 0.849, while the corresponding AUROC values for the pharmacological network model (PNM) and model based on similarity measures were 0.824 and 0.821, respectively. FFPNM with random forest (RF) is the best model among them with an AUROC value of 0.856, and the performance of FFPNM with SVM is close to that of FFPNM with RF and higher than that of FFPNM with LR. In these models, the bipartite network consisted of 152 drugs and 633 ADEs, which were obtained from the FDA Adverse Event Reporting System (FAERS) 2010 dataset. To better evaluate the performance of FFPNMs, we performed model predictions by different network consisting of 1177 drugs and 97 ADEs which were from the data of the first 120 days of FAERS 2004. FFPNM with RF achieved the best predictive result with AUROC value of 0.913. The results show that FFPNMs with ML methods, specially RF, have a superior prediction performance and robustness using only the topology features of the drug-ADE network. From our findings, the optimal, concise, and efficient models as computational methods for drug-ADE association predictions, were revealed. Source codes of this paper are available on https://github.com/Coderljl/FFPNM. INDEX TERMS Adverse drug event, prediction, complex network, machine learning, local-informationbased similarity measure, feature fusion-based predictive network model.

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

“…In [10], a new model was applied to identify medications that are more likely to result in side effects. Data were extracted from a bipartite network, where nodes stand for drugs and ADEs, and edges represent their connections.…”

Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety

“…Studies were published in relation to the development of improved statistical signal detection methodologies that showed promising results, such as vigiRank [11], combination of supervised learning and Bradford Hill's causality considerations [12], application of machine learning [13], false discovery rate detection [14,15], competition bias removal [16], and co-prescription bias and associated unmasking [17], but currently these methods are not used widely by regulatory bodies or pharmaceutical companies. In recent years, the field of network theory and analysis was researched extensively, and various methods were applied on spontaneous reporting databases in order to describe the network characteristics of spontaneous adverse event report databases [18][19][20], certain adverse events [21,22] and support decision rules [23].…”