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

ABSTRACT:

Background

Medications are essential for health care but can cause adverse drug events (ADEs), which are harmful and sometimes fatal. Detecting ADEs is a challenging task because they are often not documented in the structured data of electronic health records (EHRs). There is a need for automatically extracting ADE-related information from clinical notes, as manual review is labor-intensive and time-consuming.

Objective

This study aims to fine-tune the pretrained clinical language model, Swedish Deidentified Clinical Bidirectional Encoder Representations from Transformers (SweDeClin-BERT), for medical named entity recognition (NER) and relation extraction (RE) tasks, and to implement an integrated NER-RE approach to more effectively identify ADEs in clinical notes from clin

SUBMITTER: Kopacheva E 

PROVIDER: S-EPMC12425423 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature