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Patient Electronic Health Record as Temporal Graphs for Health Monitoring
Hugo Le Baher, Jérôme Azé, Sandra Bringay, Pascal Poncelet, Nancy Rodriguez, Caroline Dunoyer
561 - 565
10.3233/SHTI230205
Research Article
Studies in Health Technology and Informatics
Volume 302: Caring is Sharing – Exploiting the Value in Data for Health and Innovation
Abstract

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.

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