ArticleScientific reports2024
Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-Driven Preoperative Chest Radiograph Analysis for Prognostic Stratification in Surgically Resected Pathological Stage 1 Non-Small Cell Lung Cancer.Journal of imaging informatics in medicine · 2026Article
- K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction.Scientific reports · 2026Article
- Exploratory association between multimodal AI-derived digital biomarkers and in-hospital mortality in adult patients with pneumonia: A proof-of-concept study.PLOS digital health · 2026Article
- Multimodal AI-based 28-day mortality prediction of pneumonia patients at ED discharge: a multicenter study.Scientific reports · 2026Article
- Quantum-enhanced multimodal prognostic transformer for skin disease progression prediction and visualization.Scientific reports · 2026Article
- Artificial Intelligence Applications in Pneumonia: Diagnosis and Outcome Prediction.Current pulmonology reports · 2026Review
- The evolving reality of digital health.Digital healthArticle
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Authors and funding
5 authors.
Funding
Abstract
This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia between March 2020 and August 2021 were included. We developed prognostic models, including an AI-based consolidation score in addition to the conventional CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥ 65) and pneumonia severity index (PSI) for predicting pneumonia outcomes, defined as 30-day mortality during admission. A total of 489 patients, including 310 and 179 patients in training and test sets, were included. In the training set, the AI-based consolidation score on CXR was a significant variable for predicting the outcome (hazard ratio 1.016, 95% confidence interval [CI] 1.001-1.031). The model that combined CURB-65, initial O
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