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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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Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.

Elizaveta Kopacheva¹, Aron Henriksson², Hercules Dalianis²

¹Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Universitetsplatsen 1, Växjö, 352 52, Sweden, 46 737033730.

JMIR Formative Research

|September 11, 2025

Summary

This summary is machine-generated.

This study fine-tuned the SweDeClin-BERT model to automatically detect adverse drug events (ADEs) in Swedish clinical notes, achieving higher accuracy than previous methods. The integrated approach improved identification of ADEs, crucial for patient safety.

Keywords:

BERT SweDeClin-BERT adverse drug events domain-specific language models electronical health records

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Area of Science:

Natural Language Processing (NLP) in Healthcare
Clinical Informatics
Pharmacovigilance

Background:

Adverse drug events (ADEs) are a significant healthcare concern, often undocumented in structured electronic health records (EHRs).
Manual review of clinical notes for ADE detection is labor-intensive and time-consuming.
Automated extraction of ADE information from clinical text is needed for efficient detection.

Purpose of the Study:

To fine-tune the Swedish Deidentified Clinical Bidirectional Encoder Representations from Transformers (SweDeClin-BERT) model for Named Entity Recognition (NER) and Relation Extraction (RE).
To implement an integrated NER-RE pipeline for improved ADE identification in Swedish clinical notes.
To compare the performance of the fine-tuned model against traditional machine learning methods (CRFs and RF).

Main Methods:

A subset of clinical notes with suspected ADEs (ICD-10 codes A.1, A.2) from the Stockholm EPR Corpus (2009-2010) was sampled and annotated.
The SweDeClin-BERT model was fine-tuned for NER and RE tasks.
An integrated NER-RE pipeline was developed and evaluated on 395 clinical notes, followed by an error analysis.

Main Results:

62% of notes contained explicit ADE descriptions, highlighting limitations of relying solely on ICD-10 codes.
The fine-tuned SweDeClin-BERT achieved high performance: F1-score of 0.845 for NER and 0.81 for RE.
The integrated NER-RE pipeline achieved an overall F1-score of 0.81, significantly outperforming baseline models.

Conclusions:

Domain-specific language models like SweDeClin-BERT significantly improve ADE detection in clinical notes compared to conventional machine learning.
The fine-tuned model shows promise but requires further validation across different hospital settings for generalizability.
Recommendations include revising annotation guidelines and addressing challenges with compound/split entities in Swedish.