ADHD/ADD

Long-term Cognitive Network-based architecture for multi-label classification.

This paper presents a neural system to deal with multi-label classification problems that might involve sparse features. The architecture of this model involves three sequential blocks with well-defined functions. The first block consists of a multilayered feed-forward structure that extracts hidden features, thus reducing the problem dimensionality. This block is useful when dealing with sparse p...

A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications.

BACKGROUND: Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell lines and xenografts. A panel of the NCI-60 cell lines is frequently the first line to define tumor types that are optimally responsive. Open data on the gene expression of the NCI-60 cell lines, provides a unique opportunity to add another dimension ...

Environmental microorganism classification using optimized deep learning model.

Rapid environmental microorganism (EM) classification under microscopic images would help considerably identify water quality. Because of the developm...

Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy.

Recent advances in non-linear computational and dynamical modelling have opened up the possibility to parametrize dynamic neural mechanisms that drive...

Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.

Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This ...

Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach.

We aimed to delineate the neuropsychological and psychopathological profiles of children with congenital heart disease (CHD) and look for associations...

Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images.

Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, including radiotherapy planning. To further improve th...

Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis.

PURPOSE: In patients presenting with blunt hepatic injury (BHI), the utility of CT for triage to hepatic angiography remains uncertain since simple bi...

Early response to SPN-812 (viloxazine extended-release) can predict efficacy outcome in pediatric subjects with ADHD: a machine learning post-hoc analysis of four randomized clinical trials.

Machine learning (ML) was used to determine whether early response can predict efficacy outcome in pediatric subjects with ADHD treated with SPN-812. ...

DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks.

Chromosome enumeration is an essential but tedious procedure in karyotyping analysis. To automate the enumeration process, we develop a chromosome enu...

Digital Gaming Interventions in Psychiatry: Evidence, Applications and Challenges.

Human evolution has regularly intersected with technology. Digitalization of various services has brought a paradigm shift in consumerism. Treading th...

Efficacy predictors of omalizumab in Chinese patients with moderate-to-severe allergic asthma: Findings from a analysis of a randomised phase III study.

BACKGROUND: Omalizumab has demonstrated efficacy as an add-on therapy in Chinese patients with moderate-to-severe allergic asthma. This analysis asse...

Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.

Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at th...

Use of machine learning to classify adult ADHD and other conditions based on the Conners' Adult ADHD Rating Scales.

A reliable diagnosis of adult Attention Deficit/Hyperactivity Disorder (ADHD) is challenging as many of the symptoms of ADHD resemble symptoms of othe...

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients...

BrainNET: Inference of Brain Network Topology Using Machine Learning.

To develop a new functional magnetic resonance image (fMRI) network inference method, BrainNET, that utilizes an efficient machine learning algorithm...

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model.

Blood pressure monitoring is one avenue to monitor people's health conditions. Early detection of abnormal blood pressure can help patients to get ear...

Updates on Deep Learning and Glioma: Use of Convolutional Neural Networks to Image Glioma Heterogeneity.

Deep learning represents end-to-end machine learning in which feature selection from images and classification happen concurrently. This articles prov...

Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping.

BACKGROUND: Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-onl...

Biomedical Holistic Ontology for People with Rare Diseases.

This research provides a biomedical ontology to adequately represent the information necessary to manage a person with a disease in the context of a s...