ArticleJournal of imaging informatics in medicine2024
Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.
Article in Journal of imaging informatics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Teaching AI for Radiology Applications: a Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM.Journal of imaging informatics in medicine · 2026Article
- Role of artificial intelligence and point of care ultrasound in management of critically ill patients.World journal of critical care medicine · 2026Review
- Multimodal models based on radiomics and deep learning in the classification of gastric stromal tumors and gastric leiomyomas.Surgical endoscopy · 2026Article
- Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.Scientific reports · 2026Article
- Foundation models for X-ray interpretation: a narrative review of current techniques and future perspectives in diagnostic imaging.Quantitative imaging in medicine and surgery · 2026Review
- Extubation Decision Support in Critical Care: A Multimodal Machine Learning Framework Integrating Segmented Radiographs and Routine Clinical Data.Journal of imaging informatics in medicine · 2026Article
- Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.Health data science · 2026Review
- Multimodal prediction models integrating radiomics and three-dimensional deep learning for acute respiratory distress syndrome in acute pancreatitis patients.The Journal of international medical research · 2026Article
- Multimodal machine learning for 5-year mortality prediction after percutaneous coronary intervention.Scientific reports · 2025Article
- Mortality Prediction from Patient's First Day PAAC Radiograph in Internal Medicine Intensive Care Unit Using Artificial Intelligence Methods.Diagnostics (Basel, Switzerland) · 2025Article
- Teaching AI for Radiology Applications: A Multisociety‑Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM.Medical physics · 2025Article
- Multimodal predictive model for strangulation risk in adhesive small bowel obstruction using deep learning and electronic health record data.The Journal of international medical research · 2025Observational
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Authors and funding
14 authors.
Funding
Abstract
We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management.
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