ADHD/ADD

Incorporating entity-level knowledge in pretrained language model for biomedical dense retrieval.

In recent years, pre-trained language models (PLMs) have dominated natural language processing (NLP) and achieved outstanding performance in various NLP tasks, including dense retrieval based on PLMs. However, in the biomedical domain, the effectiveness of dense retrieval models based on PLMs still needs to be improved due to the diversity and ambiguity of entity expressions caused by the enrichme...

Identifying non-adult attention-deficit/hyperactivity disorder individuals using a stacked machine learning algorithm using administrative data population registers in a universal healthcare system.

BACKGROUND: This research project aims to build a Machine Learning algorithm (ML) to predict first-time ADHD diagnosis, given that it is the most frequent mental disorder for the non-adult population.

SWADAPT2: benefits of a collision avoidance assistance for powered wheelchair users in driving difficulty.

PURPOSE: In France, tens of thousands of people use a wheelchair. Driving powered wheelchairs (PWCs) present risks for users and their families. The r...

The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG.

Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention...

A robot intervention for adults with ADHD and insomnia-A mixed-method proof-of-concept study.

OBJECTIVE: To investigate individual effects of a three-week sleep robot intervention in adults with ADHD and insomnia, and to explore participants' e...

Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.

"Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children under 12 years old. Learning deficits, anxiety, depres...

Incremental learning for an evolving stream of medical ultrasound images via counterfactual thinking.

Despite the fact that traditional deep learning (DL) approaches provide promising accuracy and efficiency in medical ultrasound image analysis, they c...

Spiking neural P systems with lateral inhibition.

As a member of the third generation of artificial neural network models, spiking neural P systems (SN P systems) have gained a hot research spot in re...

Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups.

UNLABELLED: Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood that often persists into adulthood...

Respiratory signal estimation for cardiac perfusion SPECT using deep learning.

BACKGROUND: Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a ...

Rapid eye movement sleep loss associated cytomorphometric changes and neurodegeneration.

Rapid eye movement sleep (REMS) is essential for leading normal healthy living at least in higher-order mammals, including humans. In this review, we ...

Binding the Person-Specific Approach to Modern AI in the Human Screenome Project: Moving past Generalizability to Transferability.

Advances in ability to comprehensively record individuals' digital lives and in AI modeling of those data facilitate new possibilities for describing,...

Virtual agents and risk-taking behavior in adolescence: the twofold nature of nudging.

Peer pressure can influence risk-taking behavior and it is particularly felt during adolescence. With artificial intelligence (AI) increasingly presen...

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning.

The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary s...

Unveiling the benefits of multitasking in disentangled representation formation.

Johnston and Fusi recently investigated the emergence of disentangled representations when a neural network was trained to perform multiple simultaneo...

Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques.

Attention deficit hyperactivity disorder (ADHD) is considered one of the most common psychiatric disorders in childhood. The incidence of this disease...

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks.

BACKGROUND AND OBJECTIVES: Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells...

Unlock the algorithms: regulation of adaptive algorithms in reproduction.

In the USA, the Food and Drug Administration plans to regulate artificial intelligence and machine learning software systems as medical devices to imp...

Identification of attention deficit hyperactivity disorder with deep learning model.

This article explores the detection of Attention Deficit Hyperactivity Disorder, a neurobehavioral disorder, from electroencephalography signals. Due ...

Actionable artificial intelligence: Overcoming barriers to adoption of prediction tools.

Clinical prediction models based on artificial intelligence algorithms can potentially improve patient care, reduce errors, and add value to the healt...