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Article
Open AccessEvaluating regression and probabilistic methods for ECG-based electrolyte prediction
Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate b...
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Article
Open AccessDevelopment and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients
Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogr...
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Article
Open AccessDeep neural network-estimated electrocardiographic age as a mortality predictor
The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can b...
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Article
Open AccessPublisher Correction: Universal probabilistic programming offers a powerful approach to statistical phylogenetics
A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01922-8
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Article
Open AccessUniversal probabilistic programming offers a powerful approach to statistical phylogenetics
Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have see...
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Chapter and Conference Paper
Learning a Deformable Registration Pyramid
We introduce an end-to-end unsupervised (or weakly supervised) image registration method that blends conventional medical image registration with contemporary deep learning techniques from computer vision. Our...
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Reference Work Entry In depth
Deep Learning in a System Identification Perspective
The use of deep learning for sequence learning problems and system identification are intimately linked, and interesting opportunities exist on this cross section. The aim of this chapter is to briefly introdu...
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Reference Work Entry In depth
Nonlinear System Identification Using Particle Filters
Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state-space models. The particle filter constitute the most popular sequential Monte Carlo (SMC) method....
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Article
The effect of interventions on COVID-19
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Article
Open AccessAuthor Correction: Automatic diagnosis of the 12-lead ECG using a deep neural network
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Article
Open AccessAutomatic diagnosis of the 12-lead ECG using a deep neural network
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn ...
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Living Reference Work Entry In depth
Deep Learning in a System Identification Perspective
The use of deep learning for sequence learning problems and system identification are intimately linked, and interesting opportunities exist on this cross section. The aim of this chapter is to briefly introdu...
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Chapter and Conference Paper
Energy-Based Models for Deep Probabilistic Regression
While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such techniqu...
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Chapter and Conference Paper
The Eighth Visual Object Tracking VOT2020 Challenge Results
The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers publish...
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Living Reference Work Entry In depth
Nonlinear System Identification Using Particle Filters
Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state-space models. The particle filter constitute the most popular sequential Monte Carlo (SMC) method....
-
Reference Work Entry In depth
Nonlinear System Identification Using Particle Filters
Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state-space models. The particle filters constitute the most popular sequential Monte Carlo (SMC) method...
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Article
Particle Metropolis–Hastings using gradient and Hessian information
Particle Metropolis–Hastings (PMH) allows for Bayesian parameter inference in nonlinear state space models by combining Markov chain Monte Carlo (MCMC) and particle filtering. The latter is used to estimate th...
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Reference Work Entry In depth
Situational Awareness and Road Prediction for Trajectory Control Applications
Situational awareness is of paramount importance in all advanced driver assistance systems. Situational awareness can be split into the tasks of tracking moving vehicles and map** stationary objects in the i...
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Article
Particle Filter SLAM with High Dimensional Vehicle Model
This work presents a particle filter method closely related to Fastslam for solving the simultaneous localization and map** (slam) problem. Using the standard Fastslam algorithm, only low-dimensional vehicle mo...
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Article
Robust real-time tracking by fusing measurements from inertial and vision sensors
The problem of estimating and predicting position and orientation (pose) of a camera is approached by fusing measurements from inertial sensors (accelerometers and rate gyroscopes) and vision. The sensor fusio...
