Abstract
Clinical event extraction is crucial for structuring medical data, supporting clinical decision-making, and enabling other intelligent healthcare services. Traditional approaches for clinical event extraction often use pipeline-based methods to identify event triggers and elements. However, these methods commonly suffer from error propagation and information loss, leading to suboptimal performance. To address this challenge, this paper proposes an end-to-end clinical event extraction method based on the large language models (LLMs). Specifically, we transform the clinical event extraction task into an end-to-end text generation task and design a prompt learning method based on the LLMs called LMCEE. Experimental results demonstrate a significant improvement over traditional pipeline methods, with the F1 score increasing by 12%. Additionally, the proposed method outperforms the generative-based method named UIE, showcasing a 5.7% improvement in F1 score. However, the experimental results also disclose certain limitations of the proposed method, such as its sensitivity to prompt templates and its heavy dependence on the type of LLMs. These findings highlight the need for further investigation and optimization to enhance performance and robustness.