论文标题
使用知识编织的主题模型对电子健康记录数据进行建模
Modeling electronic health record data using a knowledge-graph-embedded topic model
论文作者
论文摘要
电子健康记录（EHR）数据集的快速增长为以系统的方式理解人类疾病的机会开辟了有希望的机会。但是，从EHR数据中有效提取临床知识的稀疏性和嘈杂的信息阻碍了临床知识。我们提出KG-ETM，这是一种基于端到端的知识图的多模式嵌入式主题模型。 KG-ETM通过从医学知识图中学习嵌入的嵌入来从EHR数据中提取潜在疾病主题。我们将KG-ETM应用于一个由超过100万患者组成的大规模EHR数据集。我们根据EHR的重建和药物推出评估了其性能。 KG-ETM在这两个任务上都表现出优于替代方法的卓越性能。此外，我们的模型学习了EHR代码的临床意义有意义的图形嵌入。另外，我们的模型还能够发现可解释，准确的患者表示患者分层和药物建议。
The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. KG-ETM distills latent disease topics from EHR data by learning the embedding from the medical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. KG-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations.
