Epidemiology
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Greatest papers with code
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
NeurIPS 2018
• sjblim/rmsn_nips_2018
• 
Electronic health records provide a rich source of data for machine learning methods to learn dynamic treatment responses over time.
BayesFlow: Learning complex stochastic models with invertible neural networks
In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics.
A Bayesian Monte Carlo approach for predicting the spread of infectious diseases
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.
DISEASE PREDICTION EPIDEMIOLOGY MULTIVARIATE TIME SERIES FORECASTING PROBABILISTIC PROGRAMMING
Multi-task Learning for Aggregated Data using Gaussian Processes
Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task.
AIR POLLUTION PREDICTION EPIDEMIOLOGY GAUSSIAN PROCESSES MULTI-TASK LEARNING
Multi-task Learning for Aggregated Data using Gaussian Processes
Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task.
AIR POLLUTION PREDICTION EPIDEMIOLOGY GAUSSIAN PROCESSES MULTI-TASK LEARNING
Parameter elimination in particle Gibbs sampling
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories.
Parameter elimination in particle Gibbs sampling
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories.
Spatio-Temporal Data Mining: A Survey of Problems and Methods
Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences.
Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
The first, critical, task for these applications is classifying whether a personal health event was mentioned, which we call the (PHM) problem.