AIMC Topic: Humans

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Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance.

Neural networks : the official journal of the International Neural Network Society Sep 28, 2019
In this study, we use a deep convolutional neural network (CNN) to develop a model that predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 continuous ambient monitoring stations (CAMS) across Texas. The inputs for the C...
Air Pollution Ozone Meteorology Deep Learning Time Factors Humans Environmental Monitoring Air Pollutants Neural Networks, Computer Forecasting Wind
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Comparison of CT and MRI images for the prediction of soft-tissue sarcoma grading and lung metastasis via a convolutional neural networks model.

Clinical radiology Sep 28, 2019
AIM: To realise the automated prediction of soft-tissue sarcoma (STS) grading and lung metastasis based on computed tomography (CT), T1-weighted (T1W) magnetic resonance imaging (MRI), and fat-suppressed T2-weighted MRI (FST2W) via the convolutional ...
Neural Networks, Computer Retrospective Studies Tomography, X-Ray Computed Humans Magnetic Resonance Imaging Predictive Value of Tests Neoplasm Grading Sarcoma Female Male Lung Neoplasms
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Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks.

Acta ophthalmologica Sep 27, 2019
BACKGROUND: The purpose of this study was to develop an automated diagnosis and quantitative analysis system for plus disease. The system provides a diagnostic decision but also performs quantitative analysis of the typical pathological features of t...
Diagnosis, Computer-Assisted Infant, Extremely Low Birth Weight Deep Learning ROC Curve Ranibizumab Infant, Premature Retinopathy of Prematurity Intravitreal Injections Retinal Vessels Angiogenesis Inhibitors Image Interpretation, Computer-Assisted Humans Infant, Newborn
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Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches.

The Canadian journal of cardiology Sep 27, 2019
BACKGROUND: Machine learning (ML) encompasses a wide variety of methods by which artificial intelligence learns to perform tasks when exposed to data. Although detection of myocardial infarction has been facilitated with introduction of troponins, th...
Acute Coronary Syndrome Humans Artificial Intelligence Machine Learning
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On the relationship of machine learning with causal inference.

European journal of epidemiology Sep 27, 2019
Statistics as Topic Humans Machine Learning Algorithms
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A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule.

Neural networks : the official journal of the International Neural Network Society Sep 27, 2019
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally cat...
Action Potentials Supervised Machine Learning Neurons Pattern Recognition, Automated Humans Neural Networks, Computer Neuronal Plasticity
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Retinal vascular junction detection and classification via deep neural networks.

Computer methods and programs in biomedicine Sep 27, 2019
BACKGROUND AND OBJECTIVES: The retinal fundus contains intricate vascular trees, some of which are mutually intersected and overlapped. The intersection and overlapping of retinal vessels represent vascular junctions (i.e. bifurcation and crossover) ...
Neural Networks, Computer Algorithms Humans Retina Anisotropy Retinal Vessels Image Processing, Computer-Assisted Color Fundus Oculi Databases, Factual Reproducibility of Results Diagnosis, Computer-Assisted
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Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG.

Computer methods and programs in biomedicine Sep 27, 2019
BACKGROUND AND OBJECTIVE: In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learning hierarchical feature representation automatically from raw EEG data have been proposed. However, ...
Sleep Wake Disorders Sleep Stages Electrocardiography Electroencephalography Signal Processing, Computer-Assisted Calibration Polysomnography Sleep Apnea, Obstructive Sleep Machine Learning Sensitivity and Specificity Algorithms Male Humans Time Factors Neural Networks, Computer Databases, Factual Reproducibility of Results Adult
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An overview and metanalysis of machine and deep learning-based CRISPR gRNA design tools.

RNA biology Sep 27, 2019
The CRISPR-Cas9 system has become the most promising and versatile tool for genetic manipulation applications. Albeit the technology has been broadly adopted by both academic and pharmaceutic societies, the activity (on-target) and specificity (off-t...
Humans Machine Learning CRISPR-Cas Systems Gene Editing Gene Targeting RNA, Guide, CRISPR-Cas Systems Algorithms Sensitivity and Specificity Deep Learning
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Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces.

International journal of medical informatics Sep 27, 2019
OBJECTIVES: To determine the effect of a domain-specific ontology and machine learning-driven user interfaces on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED).
Documentation Algorithms Retrospective Studies Forms and Records Control Decision Support Systems, Clinical Humans Female Emergency Service, Hospital Machine Learning Quality Improvement Case-Control Studies User-Computer Interface Male Depressive Disorder, Major
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