The rapid expansion of Electronic Medical Record (EMR) data has advanced AI-driven patient similarity computation, a key technology for intelligent healthcare. However, the handling of heterogeneous EMR formats and the integration of domain knowledge constrain existing methods. While graph-based approaches show promise, they still struggle with these issues. To address this, we propose a Large Language Model-Dynamically Enhanced Learning Network (LELN), leveraging LLMs' commonsense knowledge and reasoning to dynamically structure EMR data and enhance medical knowledge integration. LELN in tegrates two LLM-basedmodules:DS-EE(DeepSeek-Event Extraction) extracts medical events to construct structured EMR event graphs, and DS-KB (DeepSeek-Knowledge Base) infers disease-relevant knowledge to augment feature representations. The model employs a dual-stage spatial-temporal feature aggregation strategy: a Graph Attention Network captures intra- and inter-event dependencies, followed by a Bidirectional Long-Short Term Memory (BiLSTM) with attention to model temporal disease progression. Additionally, a clinical prior-guided attention mechanism emphasizes discriminative diagnostic features, improving clinical relevance. Extensive experiments on heterogeneous datasets-a real-world Chinese dataset and public MIMIC-III-show LELN outperforms baselines, achieving F1 scores of 87.66% and 85.95%, demonstrating robustness and accuracy.
Zhu et al. (Thu,) studied this question.