@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",
editor = "Househ, \{Mowafa S.\} and Househ, \{Mowafa S.\} and Tariq, \{Zain Ul Abideen\} and Mahmood Al-Zubaidi and Uzair Shah and Elaine Huesing",
booktitle = "MEDINFO 2025 - Healthcare Smart x Medicine Deep",
}
