AIMC Topic: Young Adult
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Detecting abnormal electroencephalograms using deep convolutional networks.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Nov 17, 2018
OBJECTIVES: Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of...
Brain dynamics and temporal trajectories during task and naturalistic processing.
NeuroImage
Nov 16, 2018
Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. ...
Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort.
Journal of mental health (Abingdon, England)
Nov 16, 2018
BACKGROUND: Early identification of probable post-traumatic stress disorder (PTSD) can lead to early intervention and treatment.
Social Conformity Effects on Trust in Simulation-Based Human-Robot Interaction.
Human factors
Nov 15, 2018
OBJECTIVE: We investigated the co-acting influences of communication and social conformity on trust in human-robot interaction.
Machine learning of brain gray matter differentiates sex in a large forensic sample.
Human brain mapping
Nov 15, 2018
Differences between males and females have been extensively documented in biological, psychological, and behavioral domains. Among these, sex differences in the rate and typology of antisocial behavior remains one of the most conspicuous and enduring...
Ritonavir-boosted paritaprevir, ombitasvir plus ribavirin could improve eGFR in patients with renal impairment and HCV: an Egyptian cohort.
Expert review of gastroenterology & hepatology
Nov 13, 2018
BACKGROUND: The present study aimed at evaluation of changes in estimated glomerular filtration rate (eGFR) among chronic Hepatitis C virus (HCV) patients with chronic kidney disease (CKD) Stages 3-5 who were treated with 12 weeks of ritonavir-booste...
Valine
Time Factors
Anilides
Antiviral Agents
Carbamates
Genotype
Adult
Humans
Treatment Outcome
Kidney
Male
Prospective Studies
Ribavirin
Glomerular Filtration Rate
Lactams, Macrocyclic
Renal Insufficiency, Chronic
Middle Aged
Female
Cyclopropanes
Sustained Virologic Response
Renal Dialysis
Macrocyclic Compounds
Sulfonamides
Aged
Recovery of Function
Viral Load
Hepacivirus
Young Adult
Egypt
Proline
Drug Therapy, Combination
Hepatitis C, Chronic
Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.
Magnetic resonance in medicine
Nov 12, 2018
PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response t...
A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography.
Dento maxillo facial radiology
Nov 9, 2018
OBJECTIVES:: The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification...
Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors.
International journal of environmental research and public health
Nov 9, 2018
Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforat...
Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.
PloS one
Nov 9, 2018
BACKGROUND: The purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI).
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