AIMC Topic: Normal Distribution

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An intelligent warning model for early prediction of cardiac arrest in sepsis patients.

Computer methods and programs in biomedicine Jun 11, 2019
BACKGROUND: Sepsis-associated cardiac arrest is a common issue with the low survival rate. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Several studies have be...
Male Humans Middle Aged Severity of Illness Index Machine Learning Case-Control Studies Algorithms Sepsis Electronic Health Records Female Reproducibility of Results APACHE Adolescent Young Adult Multivariate Analysis Incidence Adult Vital Signs Decision Trees Sensitivity and Specificity Normal Distribution Monitoring, Ambulatory Heart Arrest
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A novel IRBF-RVM model for diagnosis of atrial fibrillation.

Computer methods and programs in biomedicine May 29, 2019
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learn...
Humans Databases, Factual Reproducibility of Results Diagnosis, Computer-Assisted Electrocardiography Atrial Fibrillation Data Collection Heart Diseases Probability Normal Distribution Support Vector Machine Models, Statistical Bayes Theorem Signal Processing, Computer-Assisted
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A review of automated sleep stage scoring based on physiological signals for the new millennia.

Computer methods and programs in biomedicine May 2, 2019
BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiologic...
Normal Distribution Polysomnography Electrooculography Sleep Stages Electroencephalography Humans Signal Processing, Computer-Assisted Algorithms Internet Deep Learning Heart Rate Electrocardiography Medical Informatics
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Gaussian Process Trajectory Learning and Synthesis of Individualized Gait Motions.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 30, 2019
This paper proposes a Gaussian process-based method for trajectory learning and generation of individualized gait motions at arbitrary user-designated walking speeds, intended to be used in generating reference motions for robotic gait rehabilitation...
Leg Walking Speed Normal Distribution Gait Disorders, Neurologic Male Middle Aged Models, Statistical Robotics Gait Healthy Volunteers Markov Chains Anthropometry Joints Female Aged Young Adult Adult Biomechanical Phenomena Humans Algorithms
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Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms.

Computer methods and programs in biomedicine Apr 10, 2019
OBJECTIVE: A colon microarray data is a repository of thousands of gene expressions with different strengths for each cancer cell. It is necessary to detect which genes are responsible for cancer growth. This study presents an exhaustive comparative ...
Oncogenes Models, Statistical Tissue Array Analysis Regression Analysis Decision Trees Area Under Curve Bayes Theorem Humans Discriminant Analysis Neural Networks, Computer Colonic Neoplasms Machine Learning Sensitivity and Specificity Risk Normal Distribution Colon Gene Expression Profiling Logistic Models Support Vector Machine
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Selene: a PyTorch-based deep learning library for sequence data.

Nature methods Mar 28, 2019
To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for ...
Computational Biology Sequence Analysis, DNA Models, Statistical Area Under Curve Mutation Alzheimer Disease Programming Languages Mutagenesis Normal Distribution Neural Networks, Computer Software Algorithms Deep Learning Genomics Humans Gene Library
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Hybrid Degradation Equipment Remaining Useful Life Prediction Oriented Parallel Simulation considering Model Soft Switch.

Computational intelligence and neuroscience Mar 12, 2019
Equipment parallel simulation is an emerging simulation technology in recent years, and equipment remaining useful life (RUL) prediction oriented parallel simulation is an important branch of parallel simulation. An important concept in equipment par...
Probability Normal Distribution Humans Uncertainty Neural Networks, Computer Orientation, Spatial Algorithms Computer Simulation
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A Multi-Layer Gaussian Process for Motor Symptom Estimation in People With Parkinson's Disease.

IEEE transactions on bio-medical engineering Feb 18, 2019
The assessment of Parkinson's disease (PD) poses a significant challenge, as it is influenced by various factors that lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effecti...
Machine Learning Movement Humans Wrist Female Male Monitoring, Ambulatory Reproducibility of Results Accelerometry Aged Normal Distribution Middle Aged Wearable Electronic Devices Parkinson Disease Signal Processing, Computer-Assisted Hypokinesia Tremor
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A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation.

IEEE transactions on neural networks and learning systems Feb 5, 2019
Previous studies on robotic rehabilitation have shown that subjects' active participation and effort involved in rehabilitation training can promote the performance of therapies. In order to improve the voluntary effort of participants during the reh...
Humans Neural Networks, Computer Robotics Psychomotor Performance Normal Distribution Virtual Reality Exposure Therapy Photic Stimulation Upper Extremity
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A stochastic variational framework for Recurrent Gaussian Processes models.

Neural networks : the official journal of the International Neural Network Society Feb 1, 2019
Gaussian Processes (GPs) models have been successfully applied to the problem of learning from sequential observations. In such context, the family of Recurrent Gaussian Processes (RGPs) have been recently introduced with a specifically designed stru...
Learning Neural Networks, Computer Bayes Theorem Algorithms Stochastic Processes Normal Distribution
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