A Model Ensemble Approach with LLM for Chinese Text Classification

Automatic medical text categorization can assist doctors in efficiently managing patient information. By categorizing textual information such as patients’ descriptions of symptoms, doctors can easily find key information, accelerate the diagnostic process, provide superior medical advice, and successfully promote smart diagnosis and medical automated QA services. In this paper, an approach to medical text categorization is presented in the open-share task of the 9th China Conference on Health Information Processing (CHIP 2023), where complex textual relations are the two main challenges of this task. A model integration approach is proposed for this task, which can effectively solve medical text categorization through the complementary relationship of three different submodels. In addition, the solution provides external tools for targeted data enhancement for difficult samples that are hard to classify to reduce misclassification. Final results are obtained by the models through a voting mechanism. Experimental results show that the proposed method can achieve 92% accuracy and also prove the effectiveness of the model.