Improving large language models for adverse drug reactions named entity recognition via error correction prompt engineering

Abstract

The monitoring and analysis of adverse drug reactions (ADRs ) are important for ensuring patient safety and improving treatment outcomes. Accurate identification of drug names, drug components, and ADR entities during named entity recognition (NER) processes is essential for ensuring drug safety and advancing the integration of drug information. Given that existing medical name entity recognition technologies rely on large amounts of manually annotated data for training, they are often less effective when applied to adverse drug reactions due to significant data variability and the high similarity between drug names. This paper proposes a prompt template for ADR that integrates error correction examples. The prompt template includes: 1. Basic prompts with task descriptions, 2. Annotated entity explanations, 3. Annotation guidelines, 4. Annotated samples for few-shot learning, 5. Error correction examples. Additionally, it integrates complex ADR data from the web and constructs a corpus containing three types of entities (drug name, drug components, and adverse drug reactions) using the Begin, Inside, Outside (BIO) annotation method. Finally, we evaluate the effectiveness of each prompt and compare it with the fine-tuned Large Language Model Meta AI (LLaMA) model and the DeepSeek model. Experimental results show that under this prompt template, the F1 score of GPT-3.5 increased from 0.648 to 0.887, and that of GPT-4 increased from 0.757 to 0.921. It is significantly better than the fine-tuned LLaMA model and DeepSeek model. It demonstrates the superiority of the proposed method, and provides a solid foundation for extracting drug-related entity relationships and building knowledge graphs.