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Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.
Scientific reports
Dec 2, 2021
In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of r...
Fully automatic segmentation of the mandible based on convolutional neural networks (CNNs).
Orthodontics & craniofacial research
Dec 1, 2021
OBJECTIVES: To evaluate the accuracy of automatic deep learning-based method for fully automatic segmentation of the mandible from CBCTs.
A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD.
NeuroImage
Nov 30, 2021
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for s...
Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma.
British journal of cancer
Nov 30, 2021
BACKGROUND: Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of tempora...
A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study.
La Radiologia medica
Nov 25, 2021
PURPOSE: This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière's disease.
Neural correlates of word representation vectors in natural language processing models: Evidence from representational similarity analysis of event-related brain potentials.
Psychophysiology
Nov 24, 2021
Natural language processing models based on machine learning (ML-NLP models) have been developed to solve practical problems, such as interpreting an Internet search query. These models are not intended to reflect human language comprehension mechani...
Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization.
Scientific reports
Nov 23, 2021
Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes ...
Network analysis of trauma in patients with early-stage psychosis.
Scientific reports
Nov 23, 2021
Childhood trauma (ChT) is a risk factor for psychosis. Negative lifestyle factors such as rumination, negative schemas, and poor diet and exercise are common in psychosis. The present study aimed to perform a network analysis of interactions between ...
DNA methylation-based classification of malformations of cortical development in the human brain.
Acta neuropathologica
Nov 19, 2021
Malformations of cortical development (MCD) comprise a broad spectrum of structural brain lesions frequently associated with epilepsy. Disease definition and diagnosis remain challenging and are often prone to arbitrary judgment. Molecular classifica...
Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth.
Scientific reports
Nov 19, 2021
Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Wome...
Support Vector Machine
Models, Statistical
Area Under Curve
False Positive Reactions
Risk
Adolescent
ROC Curve
Logistic Models
Models, Theoretical
Postpartum Hemorrhage
Delivery, Obstetric
Labor, Obstetric
Tokyo
Neural Networks, Computer
Machine Learning
Pregnancy
Female
Infant, Newborn
Reproducibility of Results
Young Adult
Adult
Middle Aged
Deep Learning
Humans
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