基于Node2vec和知识注意力机制的诊断预测

doi:10.11896/jsjkx.210300070

Abstract

Abstract: 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.

Key words: Diagnostic prediction, Electronic health record(EHR), Gated recurrent unit(GRU), Knowledge attention, Node2vec

CLC Number:

TP391

LI Hang, LI Wei-hua, CHEN Wei, YANG Xian-ming, ZENG Cheng. Diagnostic Prediction Based on Node2vec and Knowledge Attention Mechanisms[J].Computer Science, 2021, 48(11A): 630-637.

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