Real-Time Prediction for Neonatal Endotracheal Intubation Using Multimodal Transformer Network-Reference-Cited by-同舟云学术

Real-Time Prediction for Neonatal Endotracheal Intubation Using Multimodal Transformer Network

Published:2023-06 Issue:6 Volume:27 Page:2625-2634
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Im Jueng-Eun¹,Yoon Shin-Ae²,Shin Yoon Mi³^ORCID,Park Seung⁴^ORCID

Affiliation:

1. Medical AI Research Team, Chungbuk National University Hospital, Cheongju-si, Republic of Korea

2. Department of Pediatrics, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju-si, Republic of Korea

3. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju-si, Republic of Korea

4. Biomedical Engineering, Chungbuk National University Hospital, Cheongju-si, Republic of Korea

Funder

Korea Health Industry Development Institute

Ministry of Health & Welfare, Republic of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10144459/10103154.pdf?arnumber=10103154

Reference69 articles.

1. High-risk Factors of Respiratory Distress Syndrome in Term Neonates: A Retrospective Case-control Study

2. Urban informal settlements classification via a transformer-based spatial-temporal fusion network using multimodal remote sensing and time-series human activity data;fan;Int J Appl Earth Observ Geoinf,2022

3. Predictive monitoring for respiratory decompensation leading to urgent unplanned intubation in the neonatal intensive care unit

4. TranAD

5. Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data

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