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.
Hwang et al. (Thu,) studied this question.