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Identifying epilepsy psychiatric comorbidities with machine learning.
Acta neurologica Scandinavica
Jan 22, 2020
OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine-learning classif...
Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.
Journal of the European Academy of Dermatology and Venereology : JEADV
Jan 21, 2020
BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not be...
Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach.
Applied ergonomics
Jan 20, 2020
The current research proposed and tested a structural equation model (SEM) that describes hypothesized relationships among factors affecting trust in human-robot interaction (HRI) such as trustworthiness, human-likeness, intelligence, perfect automat...
Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.
NeuroImage. Clinical
Jan 17, 2020
Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We arg...
Improving the detection of autism spectrum disorder by combining structural and functional MRI information.
NeuroImage. Clinical
Jan 17, 2020
Autism Spectrum Disorder (ASD) is a brain disorder that is typically characterized by deficits in social communication and interaction, as well as restrictive and repetitive behaviors and interests. During the last years, there has been an increase i...
Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier.
Sensors (Basel, Switzerland)
Jan 16, 2020
One of the modern trends in the design of human-machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular,...
Denoising arterial spin labeling perfusion MRI with deep machine learning.
Magnetic resonance imaging
Jan 15, 2020
PURPOSE: Arterial spin labeling (ASL) perfusion MRI is a noninvasive technique for measuring cerebral blood flow (CBF) in a quantitative manner. A technical challenge in ASL MRI is data processing because of the inherently low signal-to-noise-ratio (...
Understanding Opioid Use Disorder (OUD) using tree-based classifiers.
Drug and alcohol dependence
Jan 15, 2020
BACKGROUND: Opioid Use Disorder (OUD), defined as a physical or psychological reliance on opioids, is a public health epidemic. Identifying adults likely to develop OUD can help public health officials in planning effective intervention strategies. T...
An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy.
Sensors (Basel, Switzerland)
Jan 15, 2020
In this paper, we investigate various machine learning classifiers used in our Virtual Reality (VR) system for treating acrophobia. The system automatically estimates fear level based on multimodal sensory data and a self-reported emotion assessment....
Sub-millimeter variation in human locus coeruleus is associated with dimensional measures of psychopathology: An in vivo ultra-high field 7-Tesla MRI study.
NeuroImage. Clinical
Jan 15, 2020
The locus coeruleus (LC) has a long-established role in the attentional and arousal response to threat, and in the emergence of pathological anxiety in pre-clinical models. However, human evidence of links between LC function and pathological anxiety...
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