Patient Electronic Health Record as Temporal Graphs for Health Monitoring

被引：0

作者：

Le Baher, Hugo ^{[1
,2
,3
]}

Aze, Jerome ^{[2
]}

Bringay, Sandra ^{[1
,4
]}

Poncelet, Pascal ^{[1
]}

Rodriguez, Nancy ^{[1
]}

Dunoyer, Caroline ^{[3
,5
]}

机构：

[1] Univ Montpellier, CNRS, LIRMM, UMR 5506, Montpellier, France

[2] 5 DEGRES, Paris, France

[3] CHU Montpellier, Dept Informat Med, Montpellier, France

[4] Univ Montpellier 3, AMIS, Montpellier, France

[5] Univ Montpellier, IDESP, UMR UA11, INSERM, Montpellier, France

来源：

CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023 | 2023年 / 302卷

关键词：

Graph neural networks; MIMIC-III; health monitoring; mortality prediction; electronic health record; MORTALITY;

D O I：

10.3233/SHTI230205

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Machine learning methods are becoming increasingly popular to anticipate critical risks in patients under surveillance reducing the burden on caregivers. In this paper, we propose an original modeling that benefits of recent developments in Graph Convolutional Networks: a patient's journey is seen as a graph, where each node is an event and temporal proximities are represented by weighted directed edges. We evaluated this model to predict death at 24 hours on a real dataset and successfully compared our results with the state of the art.

引用

页码：561 / 565

页数：5

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