A Large-Scale CNN Ensemble for Medication Safety Analysis

TL;DR

This paper introduces a large-scale CNN ensemble model that analyzes user comments from healthcare forums to predict drug safety, significantly improving accuracy over traditional methods.

Contribution

It presents a novel ensemble CNN architecture with varied structures for medication safety prediction using social media data.

Findings

Achieved 87.17% accuracy in binary drug safety classification.

Achieved 62.88% accuracy in multi-class drug safety prediction.

Demonstrated superior performance over conventional approaches.

Abstract

Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing drug surveillance, and data from health-related forums and medical communities can be of a great significance for estimating such effects. In this paper, we propose an end-to-end CNN-based method for predicting drug safety on user comments from healthcare discussion forums. We present an architecture that is based on a vast ensemble of CNNs with varied structural parameters, where the prediction is determined by the majority vote. To evaluate the performance of the proposed solution, we present a large-scale dataset collected from a medical website that consists of over 50 thousand reviews for more than 4000 drugs. The results demonstrate that our model significantly outperforms conventional approaches and predicts medicine safety with an accuracy of 87.17% for binary and 62.88% for multi-classification tasks.