KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions

TL;DR

This paper introduces KESDT, a Transformer-based model enhanced with domain knowledge and data augmentation techniques, to improve adverse drug reaction detection from social media data, addressing challenges like keyword interaction, limited annotations, and class imbalance.

Contribution

The paper proposes a novel knowledge-enhanced Transformer model with shallow and deep fusion mechanisms, and uses focal loss to effectively detect ADRs in social media data.

Findings

KESDT outperforms state-of-the-art methods on three datasets.

Relative F1 improvements of 4.87%, 47.83%, and 5.73%.

Effective handling of keyword interaction, data scarcity, and class imbalance.

Abstract

Adverse drug reaction (ADR) detection is an essential task in the medical field, as ADRs have a gravely detrimental impact on patients' health and the healthcare system. Due to a large number of people sharing information on social media platforms, an increasing number of efforts focus on social media data to carry out effective ADR detection. Despite having achieved impressive performance, the existing methods of ADR detection still suffer from three main challenges. Firstly, researchers have consistently ignored the interaction between domain keywords and other words in the sentence. Secondly, social media datasets suffer from the challenges of low annotated data. Thirdly, the issue of sample imbalance is commonly observed in social media datasets. To solve these challenges, we propose the Knowledge Enhanced Shallow and Deep Transformer(KESDT) model for ADR detection. Specifically, to cope with the first issue, we incorporate the domain keywords into the Transformer model through a shallow fusion manner, which enables the model to fully exploit the interactive relationships between domain keywords and other words in the sentence. To overcome the low annotated data, we integrate the synonym sets into the Transformer model through a deep fusion manner, which expands the size of the samples. To mitigate the impact of sample imbalance, we replace the standard cross entropy loss function with the focal loss function for effective model training. We conduct extensive experiments on three public datasets including TwiMed, Twitter, and CADEC. The proposed KESDT outperforms state-of-the-art baselines on F1 values, with relative improvements of 4.87%, 47.83%, and 5.73% respectively, which demonstrates the effectiveness of our proposed KESDT.