Health Event Prediction Model Based on Dynamic and Static Features of Graph Nodes

doi:10.3969/j.issn.1006-2475.2023.10.006

Abstract

Abstract: With the wide application of electronic healthcare records （EHRs）， the prediction of clinical health events based on deep learning has attracted the attention of many researchers. The existing work mainly focuses on mining the higher-order temporal characteristics of patients， and fails to effectively learn the hidden relationship between diseases. Aiming at the problem of disease representation learning， this paper proposes a novel disease representation model （Health Event Prediction Model Based on Dynamic and Static Features of Graph Nodes， DuDas）， through which the final hidden representation of disease mined by the model contains static and dynamic information， and finally realizes the prediction of clinical tasks. Firstly， the disease graph is constructed according to the disease co-occurrence frequency， and the initial hidden representation is assigned to each disease node by the one-hot coding module. Then， the static representation of the disease is excavated according to the static mining module， and it is fused with the corresponding initial hidden representation as the initial dynamic hidden representation. The dynamic relationship between diseases is mined according to the graph convolution module to learn the final dynamic hidden representation of disease nodes. Due to the temporal nature of patient visits， this article uses gated circulation units to mine the relationship between historical diagnostic information and current diagnostic information. In order to verify the effectiveness of the proposed method， we perform experimental verification on two real datasets. Experimental results show that the proposed model in this paper reaches the higher level in the task of predicting health events.

Key words: Key words: disease representation, dynamic representation, static representation, graph neural network, feature fusion

CLC Number:

TP391

CHEN Jun-yi. Health Event Prediction Model Based on Dynamic and Static Features of Graph Nodes[J]. Computer and Modernization, 2023, 0(10): 39-44.

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