Disease Prediction
51 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Disease Prediction
Most implemented papers
CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records
astorfi/cor-gan
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To demonstrate the model fidelity, we show that CorGAN generates synthetic data with performance similar to that of real data in various Machine Learning settings such as classification and prediction.
Differentiable Graph Module (DGM) for Graph Convolutional Networks
We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning).
Disease State Prediction From Single-Cell Data Using Graph Attention Networks
vandijklab/scGAT
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To the best of our knowledge, this is the first effort to use graph attention, and deep learning in general, to predict disease state from single-cell data.
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
pydsgz/MGMC
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As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multigraph Geometric Matrix Completion (MGMC).
Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction
Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks.
The color out of space: learning self-supervised representations for Earth Observation imagery
stevinc/TheColorOutOfSpace
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We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor.
Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression
We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence.
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
mahsa91/RA-GCN
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This is accomplished by associating a graph-based neural network to each class, which is responsible for weighting the class samples and changing the importance of each sample for the classifier.
A Structural Causal Model for MR Images of Multiple Sclerosis
jcreinhold/counterfactualms
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Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?"
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
mahsa91/GKD
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The soft pseudo-labels are then used to train a deep student network for disease prediction of unseen test data for which the graph modality is unavailable.
