AENLP

This repository contains the implementation of NER inference demo from "Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in Spontaneous Reporting System"

Quick Start 🚀

The model is now available on HuggingFace Hub for easy integration!

from transformers import BertForPreTraining
from tokenization_kobert import KoBERTTokenizer

# Load model directly from HuggingFace
model = BertForPreTraining.from_pretrained("kimsiun/kaers-bert-241101")

# Initialize tokenizer
tokenizer = KoBERTTokenizer.from_pretrained('skt/kobert-base-v1')

NER Demo Setup

Try out the Named Entity Recognition demo:

pip install -r requirements.txt
streamlit run inference_demo.py

Named Entity Recognition Example

from KAERSBERTforNER import inference_NER

# Example usage
text = "프라메딘 20mg 서방정을 2주간 복용한 환자에서 목 주변의 두드러기가 발생함."
ents = inference_NER(text, print_result=True)

# Output:
# 프라메딘/DrugCompound 20mg/DrugDose 서방정을/DrugRoAFormulation 2주간/DatePeriod 복용한 환자에서
# 목/AdverseEvent 주변의/AdverseEvent 두드러기가/AdverseEvent 발생함.

print(ents)
# Returns structured entity information with positions and types

Model Details

Key Features

• Specialized for clinical and pharmaceutical domain text
• Handles Korean-English code-switching in medical contexts
• Pre-trained on 1.2M adverse drug event narratives
• Built on KoBERT architecture with domain-specific training

Performance Metrics

Task	Performance (F1-Score)
Named Entity Recognition	83.81%
Sentence Extraction	76.62%
Relation Extraction	64.37%
'Occurred' Label Classification	81.33%
'Concerned' Label Classification	77.62%

Training Specifications

• Dataset: 1.2M ADE narratives from KAERS (2015-2019)
• Focus: 'disease history' and 'adverse event' sections
• Masking rate: 15%
• Max sequence length: 200
• Learning rate: 5×10^-5

Use Cases

• Extracting drug safety information from clinical narratives
• Processing bilingual (Korean-English) medical texts
• Supporting pharmacovigilance activities
• Enhancing data quality in adverse event reporting

Limitations

• Optimized for adverse event narratives
• May not generalize well to other clinical domains
• Best suited for Korean clinical texts with English medical terminology

Citation

@article{kim2023automatic,
  title={Automatic Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in the Korea Adverse Event Reporting System Using Natural Language Processing Techniques},
  author={Kim, Siun and Kang, Taegwan and Chung, Tae Kyu and Choi, Yoona and Hong, YeSol and Jung, Kyomin and Lee, Howard},
  journal={Drug Safety},
  volume={46},
  pages={781--795},
  year={2023}
}