Rapid Response System Based on Graph Attention Network for Predicting In-Hospital Clinical Deterioration

Author:

Do Thanh-Cong1,Yang Hyung-Jeong1ORCID,Lee Guee-Sang1ORCID,Kim Soo-Hyung1ORCID,Kho Bo-Gun2

Affiliation:

1. Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South Korea

2. Department of Internal Medicine, Chonnam National University Hospital, Gwangju, South Korea

Funder

National Research Foundation of Korea

Korea government

Chonnam National University Hwasun Hospital Institute for Biomedical Science

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

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1. i-CardiAx: Wearable IoT-Driven System for Early Sepsis Detection Through Long-Term Vital Sign Monitoring;2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI);2024-05-13

2. Multi-Criteria-based Graph Neural Networks for a Medical Emergency Response System;2024 International Conference on Inventive Computation Technologies (ICICT);2024-04-24

3. Graph neural networks for clinical risk prediction based on electronic health records: A survey;Journal of Biomedical Informatics;2024-03

4. Explainable Deep Contrastive Federated Learning System for Early Prediction of Clinical Status in Intensive Care Unit;IEEE Access;2024

5. A Survey of GNN-Based Graph Similarity Learning;2023 8th International Conference on Image, Vision and Computing (ICIVC);2023-07-27

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