We develop a comprehensive framework for storing,analyzing,forecasting,and visualizing industrial energy systems consisting of multiple devices and sensors.Our framework models complex energy systems as a dynamic know...We develop a comprehensive framework for storing,analyzing,forecasting,and visualizing industrial energy systems consisting of multiple devices and sensors.Our framework models complex energy systems as a dynamic knowledge graph,utilizes a novel machine learning(ML)model for energy forecasting,and visualizes continuous predictions through an interactive dashboard.At the core of this framework is A-RNN,a simple yet efficient model that uses dynamic attention mechanisms for automated feature selection.We validate the model using datasets from two manufacturers and one university testbed containing hundreds of sensors.Our results show that A-RNN forecasts energy usage within 5%of observed values.These enhanced predictions are as much as 50%more accurate than those produced by standard RNN models that rely on individual features and devices.Additionally,A-RNN identifies key features that impact forecasting accuracy,providing interpretability for model forecasts.Our analytics platform is computationally and memory efficient,making it suitable for deployment on edge devices and in manufacturing plants.展开更多
Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and var...Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model(HGM) on electronic health record(EHR) data and used the resulting embedding vector as additional information added to a Convolutional Neural Network(CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captured the relationships between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.展开更多
基金Industry-Academia Collaboration in Energy and Manufacturing Analytics (CICP FOUNDATION) in Indiana, USAsupported by the Department of Energy, USA grant DE-SC0023349by the National Science Foundation , USA grant OAC-2339607.
文摘We develop a comprehensive framework for storing,analyzing,forecasting,and visualizing industrial energy systems consisting of multiple devices and sensors.Our framework models complex energy systems as a dynamic knowledge graph,utilizes a novel machine learning(ML)model for energy forecasting,and visualizes continuous predictions through an interactive dashboard.At the core of this framework is A-RNN,a simple yet efficient model that uses dynamic attention mechanisms for automated feature selection.We validate the model using datasets from two manufacturers and one university testbed containing hundreds of sensors.Our results show that A-RNN forecasts energy usage within 5%of observed values.These enhanced predictions are as much as 50%more accurate than those produced by standard RNN models that rely on individual features and devices.Additionally,A-RNN identifies key features that impact forecasting accuracy,providing interpretability for model forecasts.Our analytics platform is computationally and memory efficient,making it suitable for deployment on edge devices and in manufacturing plants.
文摘Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model(HGM) on electronic health record(EHR) data and used the resulting embedding vector as additional information added to a Convolutional Neural Network(CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captured the relationships between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.
