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Found 7 papers, 2 papers with code
MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction
With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data.
Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis
All these nuances make simple methods of early, late, or intermediate fusion of features inadequate for outcome prediction.
Towards Interpretable Summary Evaluation via Allocation of Contextual Embeddings to Reference Text Topics
Despite extensive recent advances in summary generation models, evaluation of auto-generated summaries still widely relies on single-score systems insufficient for transparent assessment and in-depth qualitative analysis.
On the explainability of hospitalization prediction on a large COVID-19 patient dataset
We develop various AI models to predict hospitalization on a large (over 110$k$) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021.
Chest ImaGenome Dataset for Clinical Reasoning
Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is hampered by the lack of locally labeled datasets for different findings.
Generative Feature-driven Image Replay for Continual Learning
In this work, we propose Genifer (GENeratIve FEature-driven image Replay), where a generative model is trained to replay images that must induce the same hidden features as real samples when they are passed through the classifier.
Artificial Intelligence Decision Support for Medical Triage
Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider.
