ADHD/ADD

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this st...

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 (ML) techniques to explore image diagnosis and prognosis of therapeutic lesion changes within the area of neuroradiology. However, to date, the majority of research trend and current status have not been clearly illuminated in the neuroradiology field. More than 1000 papers have been published during ...

Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data.

The availability alongside growing awareness of medicine has led to increased self-treatment of minor ailments. Self-medication is where one 'self' di...

An Increased Level of Aryl Hydrocarbon Receptor in Patients with Pancreatic Cancer.

BACKGROUND Aryl-carbon receptor (AhR), a ligand-activated transcription factor, is best known for its ability to mediate the effects of environmental ...

The Dark Side of the Moon: The Internet of Things, Industry 4.0, and The Quantified Planet.

Industry 4.0 is an innovation framework launched initially at the 2011 Hanover Fair in Germany. It is premised on extreme digital connectivity to buil...

The Combined Use of Transcranial Direct Current Stimulation and Robotic Therapy for the Upper Limb.

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of...

How Specific Abilities Might Throw '' a Curve: An Idea on How to Capitalize on the Predictive Validity of Specific Cognitive Abilities.

School grades are still used by universities and employers for selection purposes. Thus, identifying determinants of school grades is important. Broad...

Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity.

Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging f...

Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity.

Previous brain morphology-related diagnostic models for attention-deficit hyperactivity disorder (ADHD) were based on regional features. However, buil...

Classification of ADHD with bi-objective optimization.

Attention Deficit Hyperactive Disorder (ADHD) is one of the most common diseases in school aged children. In this paper, we consider using fMRI data w...

Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions.

In this study we apply two methods for data collection that are relatively new in the field of atmospheric science. The two developed methods are desi...

Fight Recognition in video using Hough Forests and 2D Convolutional Neural Network.

While action recognition has become an important line of research in computer vision, the recognition of particular events such as aggressive behavior...

MR Image Super-Resolution via Wide Residual Networks With Fixed Skip Connection.

Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image super-resolution (SR) is an effective and cost efficient a...

Assessing ADHD symptoms in children and adults: evaluating the role of objective measures.

BACKGROUND: Diagnostic guidelines recommend using a variety of methods to assess and diagnose ADHD. Applying subjective measures always incorporates r...

Reliability, validity and discriminant ability of the instrumental indices provided by a novel planar robotic device for upper limb rehabilitation.

BACKGROUND: In the last few years, there has been an increasing interest in the use of robotic devices to objectively quantify motor performance of pa...

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis.

Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, w...

An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images.

Unlike daily routine images, ultrasound images are usually monochrome and low-resolution. In ultrasound images, the cancer regions are usually blurred...

Automated EEG-based screening of depression using deep convolutional neural network.

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neur...

Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment.

Medication selection for Type 2 Diabetes (T2D) is a challenging medical decision-making problem involving multiple medications that can be prescribed ...

Using machine learning to disentangle homonyms in large text corpora.

Systematic reviews are an increasingly popular decision-making tool that provides an unbiased summary of evidence to support conservation action. Thes...