ADHD/ADD

Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals.

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and phonocardiogram (PCG), conveying abundant disease-related information, are prevalent clinical techniques for early CAD diagnosis. Nevertheless, most previous methods have relied on single-modal data, restricting their diagnosis precis...

Noise-resistant sharpness-aware minimization in deep learning.

Sharpness-aware minimization (SAM) aims to enhance model generalization by minimizing the sharpness of the loss function landscape, leading to a robust model performance. To protect sensitive information and enhance privacy, prevailing approaches add noise to models. However, additive noises would inevitably degrade the generalization and robustness of the model. In this paper, we propose a noise-...

Exploring potential ADHD biomarkers through advanced machine learning: An examination of audiovisual integration networks.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by inattention and impulsivity, linked to disruptions in func...

Investigating the impact of an AI-based play activities intervention on the quality of life of school-aged children with ADHD.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that not only impacts children's behavior, lear...

Unlocking the potential: Responsibly embracing artificial intelligence to advance the use of health data and analytics at the Canadian Institute for Health Information.

Canadian Institute for Health Information (CIHI) is looking to modernize and adopt new ways of working. This incudes the use of new technology, includ...

Evaluation of physical risk factors by fuzzy failure mode and effects analysis: an apparel mill example.

This study investigates the evaluation of risks faced by employees in a selected large-scale apparel mill using a risk assessment method with a fuzzy ...

Advancements in maize disease detection: A comprehensive review of convolutional neural networks.

This review article provides a comprehensive examination of the state-of-the-art in maize disease detection leveraging Convolutional Neural Networks (...

Using neural networks for image analysis in general physiology.

An article with three goals, namely, to (1) provide the set of ideas and information needed to understand, at a basic level, the application of convol...

Sensory Stimulation and Robot-Assisted Arm Training After Stroke: A Randomized Controlled Trial.

BACKGROUND AND PURPOSE: Functional recovery after stroke is often limited, despite various treatment methods such as robot-assisted therapy. Repetitiv...

Insights Into Detecting Adult ADHD Symptoms Through Advanced Dual-Stream Machine Learning.

Advancements in machine learning offer promising avenues for the identification of ADHD symptoms in adults, an endeavour traditionally encumbered by t...

Modeling health outcomes of air pollution in the Middle East by using support vector machines and neural networks.

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. A...

Regulatory Aspects of Artificial Intelligence and Machine Learning.

In the realm of health care, numerous generative and nongenerative artificial intelligence and machine learning (AI-ML) tools have been developed and ...

Rewireable Building Blocks for Enzyme-Powered DNA Computing Networks.

Neural networks enable the processing of large, complex data sets with applications in disease diagnosis, cell profiling, and drug discovery. Beyond e...

Siamese based deep neural network for ADHD detection using EEG signal.

BACKGROUND: Detecting Attention-Deficit/Hyperactivity Disorder (ADHD) in children is crucial for timely intervention and personalized treatment.

Integrating graph convolutional networks to enhance prompt learning for biomedical relation extraction.

BACKGROUND AND OBJECTIVE: Biomedical relation extraction aims to reveal the relation between entities in medical texts. Currently, the relation extrac...

Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study.

BACKGROUND: Chronic subdural hematoma (CSDH) represents a prevalent medical condition, posing substantial challenges in postoperative management due t...

Asynchronous Numerical Spiking Neural Membrane Systems with Local Synchronization.

Since the spiking neural P system (SN P system) was proposed in 2006, it has become a research hotspot in the field of membrane computing. The SN P sy...

Progressive Feature Fusion Attention Dense Network for Speckle Noise Removal in OCT Images.

Although deep learning for Big Data analytics has achieved promising results in the field of optical coherence tomography (OCT) image denoising, the l...

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics a...

Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during c...