基于多源健康感知数据动静态关系融合的疾病诊断

doi:10.11896/jsjkx.211100241

Abstract

Abstract: Disease diagnosis is a field of electronic health record data mining where lots of researchers are interested in,and it is also an important link to realize the intellectualization of medical diagnosis.However,due to the diversity of data sources,complex data structure and potential correlation among different types of health sensing data,there is a problem of how to fuse heterogeneous data in the process of feature extraction and data mining.Therefore,comprehensively considering clinical sensing data,personal physical record data and relationship data between diseases,and mining the latent relevant features can make the diagnosis of multi-category diseases more accurate.Dynamic and static relationship fusion of multi-source health perception data for disease diagnosis(DSRF) is proposed.Firstly,the dynamic and static relationship fusion algorithm is used to extract data correlation features and solve the heterogeneity of dynamic clinical sensing time series data and static personal physical condition data.Then the dependency matrix of multi-category diseases is calculated to extract the correlations among diseases.Finally,various health sen-sing data is fused based on the gated recurrent unit network.The comprehensive analysis of multi-source heterogeneous data is completed after the above three steps.Experimental results on the real-world American MIMIC-III clinical dataset show that the proposed model outperforms state-of-the-art models and is able to diagnose multiple categories of diseases accurately.

Key words: Multi-source data fusion, Dynamic and static relationship fusion, Disease diagnosis, Electronic health record, Clinical data mining

CLC Number:

TP399

HUO Tian-yuan, GU Jing-jing. Dynamic and Static Relationship Fusion of Multi-source Health Perception Data for Disease Diagnosis[J].Computer Science, 2022, 49(11A): 211100241-9.

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