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Article Dans Une Revue Studies in Health Technology and Informatics Année : 2023

Patient Electronic Health Record as Temporal Graphs for Health Monitoring

Résumé

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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hal-04199316 , version 1 (07-09-2023)

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Paternité - Pas d'utilisation commerciale

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Hugo Le Baher, Jérôme Azé, Sandra Bringay, Pascal Poncelet, Nancy Rodriguez, et al.. Patient Electronic Health Record as Temporal Graphs for Health Monitoring. Studies in Health Technology and Informatics, 2023, Studies in Health Technology and Informatics, Caring is Sharing – Exploiting the Value in Data for Health and Innovation (302), pp.561-565. ⟨10.3233/SHTI230205⟩. ⟨hal-04199316⟩
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