Interpretability in the medical field: A systematic mapping and review study
Context: Recently, the machine learning (ML) field has been rapidly growing, mainly owing
to the availability of historical datasets and advanced computational power. This growth is …
to the availability of historical datasets and advanced computational power. This growth is …
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 …
Causalgnn: Causal-based graph neural networks for spatio-temporal epidemic forecasting
Infectious disease forecasting has been a key focus in the recent past owing to the COVID-
19 pandemic and has proved to be an important tool in controlling the pandemic. With the …
19 pandemic and has proved to be an important tool in controlling the pandemic. With the …
A review of graph neural networks in epidemic modeling
Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …
epidemiological models. Traditional mechanistic models mathematically describe the …
Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany
During 2020, the infection rate of COVID-19 has been investigated by many scholars from
different research fields. In this context, reliable and interpretable forecasts of disease …
different research fields. In this context, reliable and interpretable forecasts of disease …
Traffic-GGNN: predicting traffic flow via attentional spatial-temporal gated graph neural networks
Y Wang, J Zheng, Y Du, C Huang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recent spatial-temporal graph-based deep learning methods for Traffic Flow Prediction
(TFP) problems have shown superior performance in modeling higher-level spatial …
(TFP) problems have shown superior performance in modeling higher-level spatial …
Heterogeneous temporal graph neural network
Graph neural networks (GNNs) have been broadly studied on dynamic graphs for their
representation learning, majority of which focus on graphs with homogeneous structures in …
representation learning, majority of which focus on graphs with homogeneous structures in …
Spatio-temporal graph learning for epidemic prediction
The COVID-19 pandemic has posed great challenges to public health services, government
agencies, and policymakers, raising huge social conflicts between public health and …
agencies, and policymakers, raising huge social conflicts between public health and …
Comparing short-term univariate and multivariate time-series forecasting models in infectious disease outbreak
Predicting infectious disease outbreak impacts on population, healthcare resources and
economics and has received a special academic focus during coronavirus (COVID-19) …
economics and has received a special academic focus during coronavirus (COVID-19) …
Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics
In this work, we aim to accurately predict the number of hospitalizations during the COVID-
19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising …
19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising …