A Large-Scale CNN Ensemble for Medication Safety Analysis
This work addresses medication safety analysis for public health surveillance, but it is incremental as it applies an existing CNN ensemble approach to a new dataset.
The paper tackled the problem of predicting drug safety from user comments on healthcare forums by proposing a large-scale CNN ensemble method, achieving 87.17% accuracy for binary classification and 62.88% for multi-classification on a dataset of over 50,000 reviews.
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.