Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time

Sunjin Hwang; Sejin Heo; Sungjun Hong; Won Chul Cha; Junsang Yoo

doi:10.3233/SHTI250898

Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time

Sunjin Hwang
, Sejin Heo
, Sungjun Hong
, Won Chul Cha
, Junsang Yoo

Department of Emergency Medicine

Sungkyunkwan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This study aimed to develop and evaluate an artificial intelligence model to predict 28-day mortality of pneumonia patients at the time of disposition from emergency department (ED). A multicenter retrospective study was conducted on data from pneumonia patients who visited the ED of a tertiary academic hospital for 8 months and from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We combined chest X-ray information, clinical data, and CURB-65 score to develop three models with the CURB-65 score as a baseline. A total of 2,874 ED visits were analyzed. The RSF model using CXR, clinical data and CURB-65 achieved a C-index of 0.872 in test set, significantly outperforming the CURB-65 score. This study developed a prediction model in pneumonia patients’ prognosis, highlighting the potential for supporting clinical decision making in ED through multi-modal clinical information.

Original language	English
Title of host publication	MEDINFO 2025 - Healthcare Smart x Medicine Deep
Subtitle of host publication	Proceedings of the 20th World Congress on Medical and Health Informatics
Editors	Mowafa S. Househ, Mowafa S. Househ, Zain Ul Abideen Tariq, Mahmood Al-Zubaidi, Uzair Shah, Elaine Huesing
Publisher	IOS Press BV
Pages	540-544
Number of pages	5
ISBN (Electronic)	9781643686080
DOIs	https://doi.org/10.3233/SHTI250898
State	Published - 7 Aug 2025
Event	20th World Congress on Medical and Health Informatics, MEDINFO 2025 - Taipei, Taiwan, Province of China Duration: 9 Aug 2025 → 13 Aug 2025

Publication series

Name	Studies in Health Technology and Informatics
Volume	329
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	20th World Congress on Medical and Health Informatics, MEDINFO 2025
Country/Territory	Taiwan, Province of China
City	Taipei
Period	9/08/25 → 13/08/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Emergency Department
Machine Learning
Prognostic Prediction

Access to Document

10.3233/SHTI250898

Cite this

Hwang, S., Heo, S., Hong, S., Cha, W. C., & Yoo, J. (2025). Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time. In M. S. Househ, M. S. Househ, Z. U. A. Tariq, M. Al-Zubaidi, U. Shah, & E. Huesing (Eds.), MEDINFO 2025 - Healthcare Smart x Medicine Deep: Proceedings of the 20th World Congress on Medical and Health Informatics (pp. 540-544). (Studies in Health Technology and Informatics; Vol. 329). IOS Press BV. https://doi.org/10.3233/SHTI250898

Hwang, Sunjin ; Heo, Sejin ; Hong, Sungjun et al. / Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time. MEDINFO 2025 - Healthcare Smart x Medicine Deep: Proceedings of the 20th World Congress on Medical and Health Informatics. editor / Mowafa S. Househ ; Mowafa S. Househ ; Zain Ul Abideen Tariq ; Mahmood Al-Zubaidi ; Uzair Shah ; Elaine Huesing. IOS Press BV, 2025. pp. 540-544 (Studies in Health Technology and Informatics).

@inproceedings{2af11a7b7fb049aab1f476270cee3b25,

title = "Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time",

abstract = "This study aimed to develop and evaluate an artificial intelligence model to predict 28-day mortality of pneumonia patients at the time of disposition from emergency department (ED). A multicenter retrospective study was conducted on data from pneumonia patients who visited the ED of a tertiary academic hospital for 8 months and from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We combined chest X-ray information, clinical data, and CURB-65 score to develop three models with the CURB-65 score as a baseline. A total of 2,874 ED visits were analyzed. The RSF model using CXR, clinical data and CURB-65 achieved a C-index of 0.872 in test set, significantly outperforming the CURB-65 score. This study developed a prediction model in pneumonia patients{\textquoteright} prognosis, highlighting the potential for supporting clinical decision making in ED through multi-modal clinical information.",

keywords = "Emergency Department, Machine Learning, Prognostic Prediction",

author = "Sunjin Hwang and Sejin Heo and Sungjun Hong and Cha, \{Won Chul\} and Junsang Yoo",

note = "Publisher Copyright: {\textcopyright} 2025 The Authors.; 20th World Congress on Medical and Health Informatics, MEDINFO 2025 ; Conference date: 09-08-2025 Through 13-08-2025",

year = "2025",

month = aug,

day = "7",

doi = "10.3233/SHTI250898",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press BV",

pages = "540--544",

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Hwang, S, Heo, S, Hong, S, Cha, WC & Yoo, J 2025, Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time. in MS Househ, MS Househ, ZUA Tariq, M Al-Zubaidi, U Shah & E Huesing (eds), MEDINFO 2025 - Healthcare Smart x Medicine Deep: Proceedings of the 20th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 329, IOS Press BV, pp. 540-544, 20th World Congress on Medical and Health Informatics, MEDINFO 2025, Taipei, Taiwan, Province of China, 9/08/25. https://doi.org/10.3233/SHTI250898

Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time. / Hwang, Sunjin; Heo, Sejin; Hong, Sungjun et al.
MEDINFO 2025 - Healthcare Smart x Medicine Deep: Proceedings of the 20th World Congress on Medical and Health Informatics. ed. / Mowafa S. Househ; Mowafa S. Househ; Zain Ul Abideen Tariq; Mahmood Al-Zubaidi; Uzair Shah; Elaine Huesing. IOS Press BV, 2025. p. 540-544 (Studies in Health Technology and Informatics; Vol. 329).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time

AU - Hwang, Sunjin

AU - Heo, Sejin

AU - Hong, Sungjun

AU - Cha, Won Chul

AU - Yoo, Junsang

PY - 2025/8/7

Y1 - 2025/8/7

N2 - This study aimed to develop and evaluate an artificial intelligence model to predict 28-day mortality of pneumonia patients at the time of disposition from emergency department (ED). A multicenter retrospective study was conducted on data from pneumonia patients who visited the ED of a tertiary academic hospital for 8 months and from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We combined chest X-ray information, clinical data, and CURB-65 score to develop three models with the CURB-65 score as a baseline. A total of 2,874 ED visits were analyzed. The RSF model using CXR, clinical data and CURB-65 achieved a C-index of 0.872 in test set, significantly outperforming the CURB-65 score. This study developed a prediction model in pneumonia patients’ prognosis, highlighting the potential for supporting clinical decision making in ED through multi-modal clinical information.

AB - This study aimed to develop and evaluate an artificial intelligence model to predict 28-day mortality of pneumonia patients at the time of disposition from emergency department (ED). A multicenter retrospective study was conducted on data from pneumonia patients who visited the ED of a tertiary academic hospital for 8 months and from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We combined chest X-ray information, clinical data, and CURB-65 score to develop three models with the CURB-65 score as a baseline. A total of 2,874 ED visits were analyzed. The RSF model using CXR, clinical data and CURB-65 achieved a C-index of 0.872 in test set, significantly outperforming the CURB-65 score. This study developed a prediction model in pneumonia patients’ prognosis, highlighting the potential for supporting clinical decision making in ED through multi-modal clinical information.

KW - Emergency Department

KW - Machine Learning

KW - Prognostic Prediction

UR - https://www.scopus.com/pages/publications/105013358527

U2 - 10.3233/SHTI250898

DO - 10.3233/SHTI250898

M3 - Conference contribution

C2 - 40775916

AN - SCOPUS:105013358527

T3 - Studies in Health Technology and Informatics

SP - 540

EP - 544

BT - MEDINFO 2025 - Healthcare Smart x Medicine Deep

A2 - Househ, Mowafa S.

A2 - Tariq, Zain Ul Abideen

A2 - Al-Zubaidi, Mahmood

A2 - Shah, Uzair

A2 - Huesing, Elaine

PB - IOS Press BV

T2 - 20th World Congress on Medical and Health Informatics, MEDINFO 2025

Y2 - 9 August 2025 through 13 August 2025

ER -

Hwang S, Heo S, Hong S, Cha WC, Yoo J. Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time. In Househ MS, Househ MS, Tariq ZUA, Al-Zubaidi M, Shah U, Huesing E, editors, MEDINFO 2025 - Healthcare Smart x Medicine Deep: Proceedings of the 20th World Congress on Medical and Health Informatics. IOS Press BV. 2025. p. 540-544. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI250898

Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time

Abstract

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UN SDGs

Keywords

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