In this paper, we investigate ensemble methods for fine-tuning transformer-based pretrained models for clinical natural language processing tasks, specifically temporal relation extraction from the clinical narrative.
To quickly assess the spatiotemporal variations of groundwater contamination under uncertain climate disturbances, we developed a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Operator (U-FNO) to solve Partial Differential Equations (PDEs) of groundwater flow and transport simulations at the site scale. We develop a combined loss function that includes both data-driven factors and physical boundary constraints at multiple spatiotemporal scales.
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance.
Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting.
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers.