End-to-end Chinese clinical event extraction based on large language model
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces LMCEE, an end-to-end method using large language models (LLMs) for clinical event extraction. LMCEE significantly improves accuracy over traditional methods, enhancing medical data structuring and decision-making.
Area Of Science
- Medical Informatics
- Natural Language Processing
- Artificial Intelligence in Healthcare
Background
- Clinical event extraction is vital for organizing medical data and powering healthcare services.
- Pipeline-based methods for event extraction face challenges like error propagation and information loss.
- Existing methods often struggle with suboptimal performance in complex clinical text.
Purpose Of The Study
- To propose an advanced, end-to-end clinical event extraction method utilizing large language models (LLMs).
- To address the limitations of traditional pipeline approaches in clinical event extraction.
- To enhance the accuracy and efficiency of structuring medical data for improved clinical decision-making.
Main Methods
- Developed LMCEE, an end-to-end method transforming clinical event extraction into a text generation task.
- Employed a prompt learning strategy tailored for LLMs to perform clinical event extraction.
- Evaluated the method's performance against traditional pipeline and existing generative-based approaches.
Main Results
- LMCEE achieved a significant 12% increase in F1 score compared to traditional pipeline methods.
- The proposed method outperformed the UIE generative-based method by 5.7% in F1 score.
- Identified limitations including sensitivity to prompt templates and LLM type dependency.
Conclusions
- End-to-end LLM-based methods offer superior performance for clinical event extraction.
- LMCEE demonstrates a promising advancement in structuring clinical data.
- Further research is needed to optimize prompt templates and LLM selection for enhanced robustness.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
