ArticleScientific reports2023
Multimodal fusion models for pulmonary embolism mortality prediction.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Artificial Intelligence-Driven Multimodal Prediction of 10-Year Incident Glaucoma Integrating Genetic and Deep Learning-Derived Imaging Features.Ophthalmology science · 2026Article
- Harnessing hybrid stacking ensemble learning for accurate pulmonary embolism diagnosis using tabular clinical data.Scientific reports · 2026Article
- Deep Learning-Based Electrocardiogram Analysis for Pulmonary Embolism Diagnosis: A Systematic Review and Meta-Analysis.Acta Cardiologica Sinica · 2026Article
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- CA-3DTransUNet with dynamic cross-scale fusion for pulmonary nodule segmentation.Scientific reports · 2026Article
- Vision-language model for report generation and outcome prediction in CT pulmonary angiogram.NPJ digital medicine · 2025Article
- Advancing Mortality Prediction in Pulmonary Embolism Using Machine Learning Algorithms-Systematic Review and Meta-Analysis.Pulmonary circulation · 2025Review
- Comparing efficiency of an attention-based deep learning network with contemporary radiological workflow for pulmonary embolism detection on CTPA: A retrospective study.European journal of radiology open · 2025Article
- Using a transformer language model to curate a pulmonary embolism dataset from the Medical Information Mart for Intensive Care IV: MIMIC-IV-Ext-PE.Research and practice in thrombosis and haemostasis · 2025Article
- Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview.Bioengineering (Basel, Switzerland) · 2025Review
- Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.Translational vision science & technology · 2025Article
- Prediction of short-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images.Journal of thrombosis and thrombolysis · 2025Article
- Advancing Clinical Practice: The Potential of Multimodal Technology in Modern Medicine.Journal of clinical medicine · 2024Article
- Large language multimodal models for new-onset type 2 diabetes prediction using five-year cohort electronic health records.Scientific reports · 2024Article
- Artificial intelligence-based pulmonary embolism classification: Development and validation using real-world data.PloS one · 2024Article
- Discovery Viewer (DV): Web-Based Medical AI Model Development Platform and Deployment Hub.Bioengineering (Basel, Switzerland) · 2023Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Pulmonary embolism (PE) is a common, life threatening cardiovascular emergency. Risk stratification is one of the core principles of acute PE management and determines the choice of diagnostic and therapeutic strategies. In routine clinical practice, clinicians rely on the patient's electronic health record (EHR) to provide a context for their medical imaging interpretation. Most deep learning models for radiology applications only consider pixel-value information without the clinical context. Only a few integrate both clinical and imaging data. In this work, we develop and compare multimodal fusion models that can utilize multimodal data by combining both volumetric pixel data and clinical patient data for automatic risk stratification of PE. Our best performing model is an intermediate fusion model that incorporates both bilinear attention and TabNet, and can be trained in an end-to-end manner. The results show that multimodality boosts performance by up to 14% with an area under the curve (AUC) of 0.96 for assessing PE severity, with a sensitivity of 90% and specificity of 94%, thus pointing to the value of using multimodal data to automatically assess PE severity.
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