Evaluating Knowledge Fusion Models on Detecting Adverse Drug Events in Text

Abstract Background Detecting adverse drug events (ADE) of drugs that are already available on the market is an essential part of the pharmacovigilance work conducted by both medical regulatory bodies and the pharmaceutical industry. Concerns regarding drug safety and economic interests serve as motivating factors for the efforts to identify ADEs. Hereby, social media platforms play an important role as a valuable source of reports on ADEs, particularly through collecting posts discussing adverse events associated with specific drugs. Methodology We aim with our study to assess the effectiveness of knowledge fusion approaches in combination with transformer-based NLP models to extract ADE mentions from diverse datasets, for instance, texts from Twitter, websites like askapatient.com, and drug labels. The extraction task is formulated as a named entity recognition (NER) problem. The proposed methodology involves applying fusion learning methods to enhance the performance of transformer-based language models with additional contextual knowledge from ontologies or knowledge graphs. Additionally, the study introduces a multi-modal architecture that combines transformer-based language models with graph attention networks (GAT) to identify ADE spans in textual data. Results A multi-modality model consisting of the ERNIE model with knowledge on drugs reached an F 1 -score of 71.84% on CADEC corpus. Additionally, a combination of a graph attention network with BERT resulted in an F 1 -score of 65.16% on SMM4H corpus. Impressively, the same model achieved an F 1 -score of 72.50% on the PSYTAR corpus, 79.54% on the ADE corpus, and 94.15% on the TAC corpus. Except for the CADEC corpus, the knowledge fusion models consistently outperformed the baseline model, BERT. Conclusion Our study demonstrates the significance of context knowledge in improving the performance of knowledge fusion models for detecting ADEs from various types of textual data. Author Summary Adverse Drug Events (ADEs) are one of the main aspects of drug safety and play an important role during all phases of drug development, including post-marketing pharmacovigilance. Negative experiences with medications are frequently reported in textual form by individuals themselves through official reporting systems or social media posts, as well as by doctors in their medical notes. Automated extraction of ADEs allows us to identify these in large amounts of text as they are produced every day on various platforms. The text sources vary highly in structure and the type of language included which imposes certain challenges on extraction systems. This work investigates to which extent knowledge fusion models may overcome these challenges by fusing structured knowledge coming from ontologies with language models such as BERT. This is of great interest since the scientific community provides highly curated resources in the form of ontologies that can be utilized for tasks such as extracting ADEs from texts.