A Deep Learning Model Using Chest Radiographs for Prediction of 30-Day Mortality in Patients With Community-Acquired Pneumonia: Development and External Validation-Reference-Cited by-同舟云学术

A Deep Learning Model Using Chest Radiographs for Prediction of 30-Day Mortality in Patients With Community-Acquired Pneumonia: Development and External Validation

Published:2023-11 Issue:5 Volume:221 Page:586-598
ISSN:0361-803X
Container-title:American Journal of Roentgenology
language:en
Short-container-title:American Journal of Roentgenology

Author:

Kim Changi¹²,Hwang Eui Jin¹,Choi Ye Ra¹³,Choi Hyewon⁴,Goo Jin Mo¹,Kim Yisak¹²,Choi Jinwook⁵,Park Chang Min¹

Affiliation:

1. Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea.

2. Interdisciplinary Program in Bioengineering and Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Korea.

3. Department of Radiology, Seoul Metropolitan Government–Seoul National University Boramae Medical Center, Seoul, Korea.

4. Department of Radiology, Chung-Ang University College of Medicine, Seoul, Korea.

5. Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.

Publisher

American Roentgen Ray Society

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Reference28 articles.

1. Community-Acquired Pneumonia Requiring Hospitalization among U.S. Adults

2. A Prediction Rule to Identify Low-Risk Patients with Community-Acquired Pneumonia

3. BTS guidelines for the management of community acquired pneumonia in adults: update 2009

4. Severity prediction rules in community acquired pneumonia: a validation study

5. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study

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