Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features-Reference-Cited by-同舟云学术

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Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features

Published:2023-03 Issue:3 Volume:15 Page:1506-1516
ISSN:2072-1439
Container-title:Journal of Thoracic Disease
language:
Short-container-title:J Thorac Dis

Author:

Kang Jieun,Kang Jiyeon,Seo Woo Jung,Park So Hee,Kang Hyung Koo,Park Hye Kyeong,Hyun JongHoon,Song Je Eun,Kwak Yee Gyung,Kim Ki Hwan,Kim Yeon Soo,Lee Sung-Soon,Koo Hyeon-Kyoung

Publisher

AME Publishing Company

Subject

Pulmonary and Respiratory Medicine

Link

https://jtd.amegroups.com/article/download/73076/pdf

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Assessing Machine Learning classifiers in COVID-19: The Role of Clinical, Laboratory, and Radiological Features in Predicting Oxygen Saturation;2024-09-08

2. Oscillometry Longitudinal Data on COVID-19 Acute Respiratory Distress Syndrome Treated with Non-Invasive Respiratory Support;Journal of Clinical Medicine;2024-03-24

3. Rapport and ethics in a digital world: impact on individuals;Digital Respiratory Healthcare;2023-12-01

4. Quantitative Analysis for Lung Disease on Thin-Section CT;Diagnostics;2023-09-18

5. Quantitative evaluation of CT scan images to determinate the prognosis of COVID-19 patient using deep learning;European Journal of Translational Myology;2023-07-25

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