Disease Prediction

49 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Deep EHR: Chronic Disease Prediction Using Medical Notes

NYUMedML/DeepEHR Machine Learning for Health Care conference 2018

Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation.

TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease

ucl-pond/MedICSS-TADPOLE 30 Aug 2018

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting future evolution of individuals at risk of Alzheimer's disease.

MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare

mp2893/mime NeurIPS 2018

Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems.

An overview of deep learning in medical imaging focusing on MRI

MMIV-ML/DLMI2018 25 Nov 2018

Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry.

Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System

mlmed/chester-xray MIDL 2019

In order to bridge the gap between Deep Learning researchers and medical professionals we develop a very accessible free prototype system which can be used by medical professionals to understand the reality of Deep Learning tools for chest X-ray diagnostics.

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

ostojanovic/BSTIM biorxiv, PLOS ONE (under review) 2019

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.

An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction

anik-UCB/CNN-CardioPrediction 1 Sep 2019

Despite a 35:1 (Non-CHD:CHD) ratio in the NHANES dataset, the investigation confirms that our proposed CNN architecture has the classification power of 77% to correctly classify the presence of CHD and 81. 8% the absence of CHD cases on a testing data, which is 85. 70% of the total dataset.

NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification

neurodram/3D-recurrent-visual-attention-model 10 Oct 2019

When further applied to the task of predicting which patients with mild cognitive impairment will be diagnosed with Alzheimer's disease within two years, the model achieves state-of-the-art accuracy with no additional training.

Representation Learning for Medical Data

KarolAntczak/multimetapath2vec 22 Jan 2020

We propose a representation learning framework for medical diagnosis domain.

CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records

astorfi/cor-gan 25 Jan 2020

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.