TY  - JOUR
AU  - Wegner, Philipp
AU  - Fröhlich, Holger
AU  - Madan, Sumit
TI  - Evaluating knowledge fusion models on detecting adverse drug events in text.
JO  - PLOS digital health
VL  - 4
IS  - 3
SN  - 2767-3170
CY  - San Francisco, CA
PB  - PLoS
M1  - DZNE-2025-00440
SP  - e0000468
PY  - 2025
AB  - 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. 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. A multi-modality model consisting of the ERNIE model with knowledge on drugs reached an F1-score of 71.84
LB  - PUB:(DE-HGF)16
C6  - pmid:40100877
DO  - DOI:10.1371/journal.pdig.0000468
UR  - https://pub.dzne.de/record/277551
ER  -