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Found 4 papers, 0 papers with code
Multimodal Sleep Apnea Detection with Missing or Noisy Modalities
Our experiments show that the proposed model outperforms other state-of-the-art approaches in sleep apnea detection using various subsets of available data and different levels of noise, and maintains its high performance (AUROC>0. 9) even in the presence of high levels of noise or missingness.
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.
mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds
Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity 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.
