Pediatrics

ADHD/ADD

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

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Showing 43-63 of 6,134 articles
Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways.

Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, d...

Unlocking the potential of wearable technology: Fitbit-derived measures for predicting ADHD in adolescents.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder ...

IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement Attacks.

Infrared (IR) images record the temperature radiation distribution of the object being captured. The...

Machine Learning Based Early Diagnosis of ADHD with SHAP Value Interpretation: A Retrospective Observational Study.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder ...

In pursuit of software solutions for pharmaceutical regulatory affairs: Insights and trends.

With rapid upsurge in technology and digital tools, the existing systems including the healthcare sy...

Detection of Cortical Arousals in Sleep Using Multimodal Wearable Sensors and Machine Learning.

Cortical arousals are brief brain activations that disrupt sleep continuity and contribute to cardio...

Learning temporal granularity with quadruplet networks for temporal knowledge graph completion.

Temporal Knowledge Graphs (TKGs) capture the dynamic nature of real-world facts by incorporating tem...

Automated Depression Detection from Text and Audio: A Systematic Review.

Depression is a prevalent mental health disorder that presents significant challenges for timely dia...

MC-ASFF-ShipYOLO: Improved Algorithm for Small-Target and Multi-Scale Ship Detection for Synthetic Aperture Radar (SAR) Images.

Synthetic aperture radar (SAR) ship detection holds significant application value in maritime monito...

The evolution and future of integrated evidence planning.

INTRODUCTION: Integrated Evidence Planning (IEP) is a strategic approach that optimizes drug develop...

Machine learning approach to single cell transcriptomic analysis of Sjogren's disease reveals altered activation states of B and T lymphocytes.

Sjogren's Disease (SjD) is an autoimmune disorder characterized by salivary and lacrimal gland dysfu...

Insulin resistance in type 1 diabetes is a key modulator of platelet hyperreactivity.

AIMS/HYPOTHESIS: Individuals with type 1 diabetes are at increased cardiovascular risk, particularly...

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

Exploring the pathogenic mechanisms of brain disorders within population is an important research in...

Monitoring Amphetamine and Methamphetamine Mixtures Based on Deep Learning Involves Colorimetric Sensing.

Precise recognition and discrimination of highly similar analytes (either in structure or property) ...

Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.

Attention deficit/hyperactivity disorder is a common neuropsychiatric disorder that affects around 5...

Cerebrospinal fluid inflammatory cytokines as prognostic indicators for cognitive decline across Alzheimer's disease spectrum.

BackgroundNeuroinflammation actively contributes to the pathophysiology of Alzheimer's disease (AD);...

Functional connectivity anomalies in medication-naive children with ADHD: Diagnostic potential, symptoms interpretation, and a mediation model.

OBJECTIVE: To identify reliable electroencephalography (EEG) biomarkers for attention deficit/hypera...

Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data.

Autism Spectrum Disorder (ASD) is a pervasive developmental disorder of the central nervous system, ...

Self-Referencing Agents for Unsupervised Reinforcement Learning.

Current unsupervised reinforcement learning methods often overlook reward nonstationarity during pre...

Enhancing brain age estimation under uncertainty: A spectral-normalized neural gaussian process approach utilizing 2.5D slicing.

Brain age gap, the difference between estimated brain age and chronological age via magnetic resonan...

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