基于Node2vec和知识注意力机制的诊断预测

Diagnostic Prediction Based on Node2vec and Knowledge Attention Mechanisms

诊断预测根据患者的历史健康状态预测未来的诊断信息,是个体化医疗决策的核心任务。电子健康记录是患者随时间推移的健康状况和临床护理的记录,它为诊断预测提供了丰富的纵向临床数据。然而,现有基于电子健康记录的诊断预测模型还不能完全了解隐藏的疾病进展模式;其次,细粒度诊断预测的性能很大程度上依赖于富含信息的特征。为了增强表达并改进学习,设计一种基于Node2vec和知识注意力的诊断预测模型。该模型基于Node2vec从医学本体的全局结构中捕捉潜在的医学知识并将诊断代码和分类代码映射为低维向量;利用分类代码嵌入向量对患者诊断的临床知识进行编码,进一步丰富患者细粒度健康状态的特征表示;设计一种知识注意力机制并与门控循环单元结合,将领域知识和电子健康记录进行融合,从患者历史健康状态中捕捉长期关联和疾病进展模式。在现实数据集上的实验结果表明,与最新方法相比,该模型显著地提高了预测性能。此外,结果表明Node2vec可以从医学本体捕捉到蕴含更多信息的医疗概念嵌入,知识注意力机制有助于促进外部知识和电子健康记录的有效融合。

Diagnostic prediction predicts the future diagnosis of patients from their historical health states,and it is the core task of personalized medical decisions.Electronic health record(EHR)documents patients’time-varying health conditions and clinical care,and also provides a wealth of longitudinal clinical data for diagnostic prediction.However,the existing diagnostic prediction models based on EHR can not completely learn the hidden disease progression patterns.Moreover,the performance of fine-grained diagnostic prediction greatly depends on more informative sequence features.In order to improve the performance,we propose adiagnostic prediction model,called Node2vec and knowledge attention model(NKAM).Specifically,based on Node2vec,the model captures the potential medical knowledge from the global structure of medical ontology.It also maps categories into low-dimensional vectors and encodes the medical knowledge of patients’health state into category embedding vectors.The diagnosis code embedding vectorsare used to enrich the patients’fine-grained health state representation.Then,the long-term dependencies and disease progression patterns can be extracted from the patient’s historical health states using a knowledge attention mechanism combined with the Gated Recurrent Unit(GRU).Experimental results on real-world dataset show that NKAM significantly improves the prediction performance compared with state-of-the-art methods.Furthermore,the experiments reveal that Node2vec can capture more informative medical concept embedding from medical ontology,and the knowledge-based attention mechanism helps to the effective integration of external knowledge and electronic health records.