Improving Drug Identification in Overdose Death Surveillance using Large Language Models
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View abstract on PubMed
Summary
This summary is machine-generated.Natural language processing (NLP) models, specifically BioClinicalBERT, accurately classify drug-involved overdose deaths from free-text reports. This enhances timely surveillance, overcoming limitations of manual coding for emerging substance use trends.
Area Of Science
- Public Health Surveillance
- Computational Linguistics
- Data Science
Background
- Drug-related deaths, primarily fentanyl-driven, necessitate rapid and precise surveillance in the U.S.
- Current methods relying on manual coding of coroner reports into ICD-10 classifications cause data delays and loss.
- Existing natural language processing (NLP) applications for overdose surveillance have shown limitations.
Purpose Of The Study
- To evaluate and compare various NLP models for classifying specific drug involvement from unstructured death certificate text.
- To assess the performance of traditional machine learning, general-domain BERT, and large language models (LLMs) against fine-tuned clinical NLP models.
- To determine the efficacy of NLP in automating and enhancing overdose surveillance data extraction.
Main Methods
- Utilized a dataset of 35,433 U.S. death records from 2020 for training and internal testing.
- Performed external validation on a separate dataset of 3,335 records from 2023-2024.
- Compared traditional classifiers, Bidirectional Encoder Representations from Transformers (BERT), BioClinicalBERT, Qwen 3, and Llama 3 using macro-averaged F1 scores.
Main Results
- Fine-tuned BioClinicalBERT models achieved near-perfect performance (macro F1 >=0.998) on the internal test set.
- External validation demonstrated the robustness of BioClinicalBERT (macro F1=0.966), outperforming other evaluated models.
- NLP models significantly outperformed conventional machine learning and general-domain BERT and LLMs.
Conclusions
- Fine-tuned clinical NLP models, like BioClinicalBERT, provide a highly accurate and scalable solution for classifying drug-involved overdose deaths from free-text reports.
- These NLP methods can substantially accelerate surveillance workflows, surpassing the limitations of manual ICD-10 coding.
- The approach supports near real-time detection of emerging substance use trends, improving public health response.
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