ADHD/ADD

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 assessed the potential predictors for the efficacy of omalizumab in these patients.

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 the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for computational screening methods and requires the development of new techniques for efficient exploration. We present an automated workflow that system...

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...

Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.

OBJECTIVE: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biom...

BMIVPOT, a Fully Automated Version of the Intravenous Pole: Simulation, Design, and Evaluation.

Robotic intravenous poles are automated supportive instrument that needs to be triggered by patients to hold medications and needed supplies. Healthca...

Artificial Intelligence Solutions for Analysis of X-ray Images.

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care...

Investigation of MDMA Inhibitory Effect on CytochromeP450 3A4 in Isolated Perfused Rat Liver Model Using Tramadol.

MDMA (methylenedioxymethamphetamine) is a synthetic compound, which is a structurally derivative of amphetamine. Also, it acts like an amphetamine, s...

A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure.

We created a facet atlas that maps the interrelations between facet scales from 13 hierarchical personality inventories to provide a practically usefu...

Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers.

With the tremendous development of artificial intelligence, many machine learning algorithms have been applied to the diagnosis of human cancers. Rece...

High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks.

Aiming at high-resolution radar target recognition, new convolutional neural networks, namely, Inception-based VGG (IVGG) networks, are proposed to cl...

Identification of competing neural mechanisms underlying positive and negative perceptual hysteresis in the human visual system.

Hysteresis is a well-known phenomenon in physics that relates changes in a system with its prior history. It is also part of human visual experience (...

CAB U-Net: An end-to-end category attention boosting algorithm for segmentation.

With the development of machine learning and artificial intelligence, many convolutional neural networks (CNNs) based segmentation methods have been p...

Fully convolutional attention network for biomedical image segmentation.

In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks e...

Is P&T Ready to Add Rapid Cycle Analytics to Formulary?

The intent of this article is to evaluate a novel approach, using rapid cycle analytics and real world evidence, to optimize and improve the medicati...