Exploiting Question-Answer framework with multi-GRU to detect Adverse Drug Reaction on social media

Abstract

Adverse Drug Reactions (ADRs) stand out as a pressing challenge in public health and a critical aspect of drug discovery. The dilemma arises from the inherent impossibility of conducting a comprehensive evaluation of a drug before its market release, constrained by the limitations in scale and duration of clinical trials. Therefore, the post-marketing detection of ADRs in a timely and accurate manner becomes imperative. Adding to the complexity, a multitude of tweets harbor concealed information about adverse drug reactions, creating difficulties due to their concise, sporadic, and noisy content. To solve the problem, we regard ADR detection as a question-answer problem and introduces an innovative neural network framework with multiple GRU layers designed for extracting ADR-related information from tweets. The Von Mises–Fisher distribution (vMF distribution) is applied to derive keyword vectors through tweet sampling. An attention mecha-nism is employed to enhance the interaction between these keyword vectors and the word sequences within tweets. The credibility of word sequences is systematically evaluated based on the reliability of answer factors. To address concerns related to background information and training speed, we propose a quality assurance mechanism utilizing a GRU network due to its straightforward structure and efficient training capabilities. As a result of the training process, word sequences are mapped to a low-latitude vector space, generating corresponding answers. Experimental results obtained from two Twitter ADR datasets affirm that our Question-Answer Mechanism, leveraging multi-GRU architecture, significantly improves the accuracy of ADR detection in tweets. Our method achieved F1-scores of 81.3% and 73.3% on the two datasets, respectively, while consistently maintaining a higher recall.