Neurology

Autism

Latest AI and machine learning research in autism for healthcare professionals.

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Toward a motor signature in autism: Studies from human-machine interaction.

BACKGROUND: Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders ...

Machine learning studies on major brain diseases: 5-year trends of 2014-2018.

In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (M...

Mobile detection of autism through machine learning on home video: A development and prospective validation study.

BACKGROUND: The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20...

Multi-Objective Genetic Algorithm Assisted by an Artificial Neural Network Metamodel for Shape Optimization of a Centrifugal Blood Pump.

A centrifugal blood pump is a common type of the pump used as a left ventricular assist device (LVAD...

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors.

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading ca...

Pectin extraction from Helianthus annuus (sunflower) heads using RSM and ANN modelling by a genetic algorithm approach.

In this work, Response Surface Methodology (RSM) and Artificial Neural Network coupled with genetic ...

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application.

BACKGROUND: Electronic health records (EHRs) bring many opportunities for information utilization. O...

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.

The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, an...

Prenatal Exposure to Gestational Nicotine before Neurulation is Detrimental to Neurodevelopment of Wistar Rats' Offspring.

BACKGROUND AND AIM: This study aimed to determine the effect of gestational nicotine exposure before...

Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm.

Prediction of sediment volume and sediment load is always one of the important issues for decision-m...

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimi...

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.

Machine learning is becoming an increasingly popular approach for investigating spatially distribute...

Selection of recycling centre locations by using the interval type-2 fuzzy sets and two-objective genetic algorithm.

In the sense of contemporary industrial challenges, it may be said that many issues in the field of ...

Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods.

We investigated whether machine learning methods could potentially identify a subgroup of persons wi...

Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification.

Effective utilization of multi-center data for autism spectrum disorder (ASD) diagnosis recently has...

Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors.

Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by material...

An accessible and efficient autism screening method for behavioural data and predictive analyses.

Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can su...

Memetic Search Optimization Along with Genetic Scale Recurrent Neural Network for Predictive Rate of Implant Treatment.

Implant treatment is one of the most important surgical processes in teeth which reduces the difficu...

Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets.

Deep learning has become the new state-of-the-art for many problems in image analysis. However, larg...

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