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Pharmacovigilance01:19

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
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Allergic reactions related to drugs are hypersensitivity responses driven by the immune system and bear no connection to the drug's therapeutic action. While drugs in isolation do not trigger an immune response, they can interact with endogenous proteins to form antigens. These antigens stimulate lymphocytes to produce antibodies. IgE-type antibodies attach themselves to mast cells. Upon subsequent exposure to the same stimulus, the antigen-antibody interaction is initiated, unleashing...
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Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

Christopher McMaster1, Julia Chan2, David F L Liew3

  • 1Department of Clinical Pharmacology & Therapeutics, Austin Health, Melbourne, Victoria, Australia; Department of Rheumatology, Austin Health, Melbourne, Victoria, Australia; The Centre for Digital Transformation of Health, University of Melbourne, Melbourne, Victoria, Australia; School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia.

Journal of Biomedical Informatics
|December 4, 2022
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Summary

A new deep learning algorithm accurately detects adverse drug reactions (ADRs) in clinical notes. This automated approach improves upon traditional reporting methods, enhancing patient safety and pharmacovigilance efforts.

Keywords:
Adverse drug reactionsMachine learningNatural language processingTransfer learning

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Area of Science:

  • Pharmacovigilance
  • Clinical Informatics
  • Natural Language Processing

Background:

  • Adverse drug reactions (ADRs) are a major cause of patient morbidity and hospital admissions worldwide.
  • Traditional spontaneous ADR reporting is limited by significant under-reporting, hindering pharmacovigilance.
  • Automated reporting offers a potential solution but risks over-reporting non-ADR events.

Purpose of the Study:

  • To develop and evaluate a deep learning natural language processing (NLP) algorithm for automated ADR detection in hospital discharge summaries.
  • To enhance ADR detection accuracy by differentiating true ADRs from other drug-related adverse events.
  • To compare the performance of the developed algorithm against existing models.

Main Methods:

  • A deep learning model (DeBERTa) was pre-trained on 1.1 million clinical documents and fine-tuned on 861 annotated discharge summaries.
  • The model was trained to specifically identify mentions of ADRs.
  • Performance was evaluated against a baseline model and a pre-trained RoBERTa model, with an annotated corpus enriched for validated ADRs and confounding events.

Main Results:

  • The developed deep learning algorithm achieved a ROC-AUC of 0.955 (95% CI 0.933 - 0.978) for identifying discharge summaries containing ADR mentions.
  • The model significantly outperformed two comparator models in ADR detection.
  • The algorithm demonstrated effectiveness in differentiating ADRs from other drug-related adverse events.

Conclusions:

  • Deep learning NLP models can effectively and accurately detect adverse drug reactions in clinical text.
  • This automated approach shows promise for improving pharmacovigilance and patient safety by increasing ADR reporting rates.
  • The developed algorithm represents a significant advancement in automated ADR detection from electronic health records.