Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Examining covid-19 forecasting using spatio-temporal graph neural networks
In this work, we examine a novel forecasting approach for COVID-19 case prediction that
uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting …
uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting …
A survey of deep learning for electronic health records
Medical data is an important part of modern medicine. However, with the rapid increase in
the amount of data, it has become hard to use this data effectively. The development of …
the amount of data, it has become hard to use this data effectively. The development of …
Transfer graph neural networks for pandemic forecasting
The recent outbreak of COVID-19 has affected millions of individuals around the world and
has posed a significant challenge to global healthcare. From the early days of the pandemic …
has posed a significant challenge to global healthcare. From the early days of the pandemic …
On the equivalence between temporal and static equivariant graph representations
This work formalizes the associational task of predicting node attribute evolution in temporal
graphs from the perspective of learning equivariant representations. We show that node …
graphs from the perspective of learning equivariant representations. We show that node …
STAN: spatio-temporal attention network for pandemic prediction using real-world evidence
Objective We aim to develop a hybrid model for earlier and more accurate predictions for the
number of infected cases in pandemics by (1) using patients' claims data from different …
number of infected cases in pandemics by (1) using patients' claims data from different …
Inter-series attention model for COVID-19 forecasting
COVID-19 pandemic has an unprecedented impact all over the world since early 2020.
During this public health crisis, reliable forecasting of the disease becomes critical for …
During this public health crisis, reliable forecasting of the disease becomes critical for …
Heterogeneous temporal graph transformer: An intelligent system for evolving android malware detection
The explosive growth and increasing sophistication of Android malware call for new
defensive techniques to protect mobile users against novel threats. To address this …
defensive techniques to protect mobile users against novel threats. To address this …
[HTML][HTML] Long-term prediction for temporal propagation of seasonal influenza using Transformer-based model
L Li, Y Jiang, B Huang - Journal of biomedical informatics, 2021 - Elsevier
Influenza is one of the most common infectious diseases worldwide, which causes a
considerable economic burden on hospitals and other healthcare costs. Predicting new and …
considerable economic burden on hospitals and other healthcare costs. Predicting new and …
Tdefsi: Theory-guided deep learning-based epidemic forecasting with synthetic information
Influenza-like illness (ILI) places a heavy social and economic burden on our society.
Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse …
Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse …