Pediatrics

ADHD/ADD

Latest AI and machine learning research in adhd/add for healthcare professionals.

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Showing 106-126 of 6,134 articles
Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.

Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmenta...

TPAFNet: Transformer-Driven Pyramid Attention Fusion Network for 3D Medical Image Segmentation.

The field of 3D medical image segmentation is witnessing a growing trend in the utilization of combi...

Modeling Functional Brain Networks for ADHD via Spatial Preservation-Based Neural Architecture Search.

Modeling functional brain networks (FBNs) for attention deficit hyperactivity disorder (ADHD) has sp...

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 preci...

Noise-resistant sharpness-aware minimization in deep learning.

Sharpness-aware minimization (SAM) aims to enhance model generalization by minimizing the sharpness ...

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 inattent...

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 disord...

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...

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 de...

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 under...

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...

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 ...

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, ...

Regulatory Aspects of Artificial Intelligence and Machine Learning.

In the realm of health care, numerous generative and nongenerative artificial intelligence and machi...

Rewireable Building Blocks for Enzyme-Powered DNA Computing Networks.

Neural networks enable the processing of large, complex data sets with applications in disease diagn...

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

BACKGROUND: Detecting Attention-Deficit/Hyperactivity Disorder (ADHD) in children is crucial for tim...

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

BACKGROUND AND OBJECTIVE: Biomedical relation extraction aims to reveal the relation between entitie...

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 hotsp...

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