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Pharmacovigilance

Pharmacovigilance

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.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...

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Named Entity Recognition in Pubmed Abstracts for Pharmacovigilance Using Deep Learning.

T Trang Nghiem¹, Cedric Bousquet^2,3

¹Institute of Thermal, Mechanical and Material Sciences (ITheMM EA 7548), University of Reims Champagne-Ardenne, 51687 Reims, France.

Studies in Health Technology and Informatics

|May 25, 2022

Summary

Natural language processing and deep learning effectively detect adverse drug reactions in medical literature. This method achieved an 87.8% F-measure, paving the way for a comprehensive drug reaction database.

Keywords:

Artificial intelligence Deep learning Pubmed pharmacovigilance

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Area of Science:

Computational linguistics
Pharmacovigilance
Artificial intelligence in medicine

Background:

Adverse drug reactions (ADRs) are a significant concern in patient safety.
Identifying ADRs from biomedical literature, such as PubMed abstracts, is crucial for pharmacovigilance.
Existing methods require robust computational approaches for efficient ADR detection.

Purpose of the Study:

To develop and evaluate a deep learning model for detecting adverse drug reactions in PubMed abstracts.
To assess the performance of a Bi-LSTM with a CRF layer for named entity recognition of drugs and ADRs.
To explore future improvements using advanced models like BERT for enhanced ADR detection.

Main Methods:

Utilized deep learning, specifically a Bidirectional Long Short-Term Memory (Bi-LSTM) network.
Incorporated a Conditional Random Field (CRF) layer for sequence labeling of drugs and adverse reactions.
Trained and evaluated the model on a dataset of PubMed abstracts, building upon prior work.

Main Results:

The implemented deep learning classifier achieved a high F-measure of 87.8% for ADR detection.
Demonstrated the effectiveness of Bi-LSTM and CRF layers in recognizing drug and adverse reaction entities.
Established a strong baseline for automated ADR identification from scientific literature.

Conclusions:

Deep learning models, particularly Bi-LSTM-CRF, are highly effective for detecting adverse drug reactions in PubMed abstracts.
The developed classifier shows significant potential for improving pharmacovigilance efforts.
Future work involving BERT could further enhance detection accuracy and enable the creation of a large-scale ADR database.