Electronic Health Record (EHR) systems are particularly valuable in pediatrics due to high barriers in clinical studies, but pediatric EHR data often suffer from low content density. Existing EHR code embeddings tailored for the general patient population fail to address the unique needs of pediatric patients. To bridge this gap, we introduce a transfer learning approach, MUltisource Graph Synthesis (MUGS), aimed at accurate knowledge extraction and relation detection in pediatric contexts. MUGS integrates graphical data from both pediatric and general EHR systems, along with hierarchical medical ontologies, to create embeddings that adaptively capture both the homogeneity and heterogeneity between hospital systems. These embeddings enable refined EHR feature engineering and nuanced patient profiling, proving particularly effective in identifying pediatric patients similar to specific profiles, with a focus on pulmonary hypertension (PH). MUGS embeddings, resistant to negative transfer, outperform other benchmark methods in multiple applications, advancing evidence-based pediatric research.

由于临床研究的门槛很高，电子健康记录 （EHR） 系统在儿科中特别有价值，但儿科 EHR 数据通常内容密度低。为普通患者群体量身定制的现有 EHR 代码嵌入无法满足儿科患者的独特需求。为了弥合这一差距，我们引入了一种迁移学习方法，即 MUltisource Graph Synthesis （MUGS），旨在在儿科环境中进行准确的知识提取和关系检测。MUGS 集成了来自儿科和一般 EHR 系统的图形数据，以及分层医学本体，以创建自适应捕获医院系统之间同质性和异质性的嵌入。这些嵌入可实现精细的 EHR 特征工程和细致入微的患者分析，事实证明，在识别类似于特定特征的儿科患者方面特别有效，重点是肺动脉高压 （PH）。MUGS 包埋物具有抗负转移性，在多种应用中优于其他基准方法，推进了循证儿科研究。