Research on KG and LLM knowledge-enhanced pediatric diseases intelligent diagnosis

Pediatric diseases are challenging to diagnose due to their complex and diverse characteristics. To assist doctors in diagnosis and help them make informed decisions, this paper proposes a Knowledge graph and Large language model Knowledge-Enhanced (KLKE) intelligent diagnosis model. The intelligent diagnosis task is treated as a text classification task, where the original Electronic Medical Record are input into MacBERT model encoder to obtain the contextual representation after key information enhancement and KG prompted LLM enhancement respectively. The final text representation is obtained by concatenating and merging the enhanced representations. Graph Convolutional Network is utilized to obtain the knowledge representation and the two representations are fused using a fusion method based on interactive attention mechanism. Experiments are conducted on PeEMR, and compared with models that only fuses triples and graph structures. The KLKE achieved an increase of 9.15% and 2.28% in F1_micro scores respectively.