ArticleScientific reports2023
Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Visual prompt engineering for multimodal and irregularly sampled medical data.Communications medicine · 2026Article
- Role of artificial intelligence and point of care ultrasound in management of critically ill patients.World journal of critical care medicine · 2026Review
- Bayesian Optimization of Catalysis with In-Context Learning.ACS central science · 2026Article
- FA-DeepMSM: a few-shot adapted interpretable multimodal survival model for improved prognostic prediction in glioblastoma.Scientific reports · 2026Article
- A data-centric perspective on designing AI foundation models for healthcare.Frontiers in digital health · 2026Article
- Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.Health data science · 2026Review
- Assessment of transformer-based AI in clinical oncology.BMC medical informatics and decision making · 2025Article
- From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization.Journal of anesthesia and translational medicine · 2025Review
- The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative reviewDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Enhanced nuclear information fusion and visual transformer for pathological breast cancer image classification.Scientific reports · 2025Article
- Chronic Ulcers Healing Prediction through Machine Learning Approaches: Preliminary Results on Diabetic Foot Ulcers Case Study.Journal of clinical medicine · 2025Article
- A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records.JAMIA open · 2025Article
- Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review.BMC medical education · 2024Review
- Advances in AI and machine learning for predictive medicine.Journal of human genetics · 2024Review
- Improving Triage Accuracy in Prehospital Emergency Telemedicine: Scoping Review of Machine Learning-Enhanced Approaches.Interactive journal of medical research · 2024Article
- Identifying the need for infection-related consultations in intensive care patients using machine learning models.Scientific reports · 2024Article
- AI approaches for the discovery and validation of drug targets.Cambridge prisms. Precision medicine · 2024Review
- Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When clinicians assess the prognosis of patients in intensive care, they take imaging and non-imaging data into account. In contrast, many traditional machine learning models rely on only one of these modalities, limiting their potential in medical applications. This work proposes and evaluates a transformer-based neural network as a novel AI architecture that integrates multimodal patient data, i.e., imaging data (chest radiographs) and non-imaging data (clinical data). We evaluate the performance of our model in a retrospective study with 6,125 patients in intensive care. We show that the combined model (area under the receiver operating characteristic curve [AUROC] of 0.863) is superior to the radiographs-only model (AUROC = 0.811, p < 0.001) and the clinical data-only model (AUROC = 0.785, p < 0.001) when tasked with predicting in-hospital survival per patient. Furthermore, we demonstrate that our proposed model is robust in cases where not all (clinical) data points are available.
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Registered trials
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