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Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.

Scientific reports Dec 30, 2019
Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a di...
Humans Neural Networks, Computer Female Reproducibility of Results Biomarkers, Tumor Neoplasm Grading Mutation Promoter Regions, Genetic Isocitrate Dehydrogenase Telomerase Male Image Processing, Computer-Assisted Glioma Magnetic Resonance Imaging Neoplasm Staging
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Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.

Arthritis research & therapy Dec 30, 2019
BACKGROUND: Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We devel...
Adult Sensitivity and Specificity Electronic Health Records Male Scleroderma, Systemic Middle Aged Female Humans International Classification of Diseases Reproducibility of Results Machine Learning Aged Algorithms Aged, 80 and over Databases, Factual
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Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department.

BMC medical informatics and decision making Dec 30, 2019
OBJECTIVE: To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated...
United States Socioeconomic Factors Triage Humans Natural Language Processing Infant Emergency Service, Hospital Tomography, X-Ray Computed Radiography Adolescent Health Care Surveys Female Child, Preschool Patient Acceptance of Health Care Male Child Logistic Models
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A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis.

Computational and mathematical methods in medicine Dec 30, 2019
Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegene...
Diagnosis, Computer-Assisted Cognitive Dysfunction Neuroimaging Image Processing, Computer-Assisted Humans Aged Databases, Factual Magnetic Resonance Imaging Alzheimer Disease Machine Learning Male Discriminant Analysis Biomarkers Algorithms Positron-Emission Tomography Middle Aged Brain Image Interpretation, Computer-Assisted Disease Progression Female Linear Models Reproducibility of Results Pattern Recognition, Automated
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A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.

PloS one Dec 30, 2019
'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previously been based on longitudinal wheeze patterns or hypothesis-driven observational criteria. The aim ...
Eosinophils Medical History Taking Leukocyte Count Respiratory Sounds Twins Diseases in Twins Male Severity of Illness Index Adolescent Child Regression Analysis Female Sweden Risk Factors Humans Asthma Machine Learning Data Analysis Biomarkers
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Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose-volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy.

Medical physics Dec 29, 2019
PURPOSE: We propose a novel domain-specific loss, which is a differentiable loss function based on the dose-volume histogram (DVH), and combine it with an adversarial loss for the training of deep neural networks. In this study, we trained a neural n...
Deep Learning Prostatic Neoplasms Radiotherapy Planning, Computer-Assisted Radiation Dosage Radiotherapy Dosage Humans Male
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Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.

International journal of medical informatics Dec 28, 2019
BACKGROUND: The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for experienced multidisciplinary cent...
Humans Algorithms Prognosis Finland Female Middle Aged Aged Brazil Aged, 80 and over Adolescent Young Adult Child Neoplasm Recurrence, Local Incidence Adult Support Vector Machine Combined Modality Therapy Cohort Studies Male Decision Trees Supervised Machine Learning Bayes Theorem Tongue Neoplasms Mouth Neoplasms Chemoradiotherapy
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Using machine learning to model problematic smartphone use severity: The significant role of fear of missing out.

Addictive behaviors Dec 28, 2019
We examined a model of psychopathology variables, age and sex as correlates of problematic smartphone use (PSU) severity using supervised machine learning in a sample of Chinese undergraduate students. A sample of 1097 participants completed measures...
China Models, Statistical Supervised Machine Learning Internet Addiction Disorder Humans Female Young Adult Male Students
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Prediction and optimization of slaughter weight in meat-type quails using artificial neural network modeling.

Poultry science Dec 28, 2019
Carcass yield of meat-type quails is strongly correlated with the weight of the birds at slaughter (slaughter weight [SW]; body weight at 45 D of age). Moreover, prediction of superior animals for SW at the earlier stages of the rearing period is fav...
Sex Factors Abattoirs Animal Husbandry Neural Networks, Computer Ovum Female Coturnix Animals Models, Biological Male Body Weight
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A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis.

NeuroImage. Clinical Dec 28, 2019
INTRODUCTION: Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w lesions on brain MR scans is considered a predictive biomarker for the diseas...
Longitudinal Studies Magnetic Resonance Imaging Multiple Sclerosis Image Interpretation, Computer-Assisted Neuroimaging Female Adult Male Middle Aged Deep Learning Humans Biomarkers Brain
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