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@ARTICLE{Wegner:277551,
      author       = {Wegner, Philipp and Fröhlich, Holger and Madan, Sumit},
      title        = {{E}valuating knowledge fusion models on detecting adverse
                      drug events in text.},
      journal      = {PLOS digital health},
      volume       = {4},
      number       = {3},
      issn         = {2767-3170},
      address      = {San Francisco, CA},
      publisher    = {PLoS},
      reportid     = {DZNE-2025-00440},
      pages        = {e0000468},
      year         = {2025},
      abstract     = {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 $71.84\%$ on CADEC corpus.
                      Additionally, a combination of a graph attention network
                      with BERT resulted in an F1-score of $65.16\%$ on SMM4H
                      corpus. Impressively, the same model achieved an F1-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. 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.},
      cin          = {Clinical Research Platform (CRP)},
      ddc          = {610},
      cid          = {I:(DE-2719)1011401},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40100877},
      doi          = {10.1371/journal.pdig.0000468},
      url          = {https://pub.dzne.de/record/277551},
}