Pediatrics

ADHD/ADD

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

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Unveiling the benefits of multitasking in disentangled representation formation.

Johnston and Fusi recently investigated the emergence of disentangled representations when a neural ...

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 dis...

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 ...

Unlock the algorithms: regulation of adaptive algorithms in reproduction.

In the USA, the Food and Drug Administration plans to regulate artificial intelligence and machine l...

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

Clinical prediction models based on artificial intelligence algorithms can potentially improve patie...

Identification of attention deficit hyperactivity disorder with deep learning model.

This article explores the detection of Attention Deficit Hyperactivity Disorder, a neurobehavioral d...

Evaluating platelet concentrates by platelet indices, thromboelastography, and flow cytometry.

INTRODUCTION: Platelet transfusion has been therapeutically used in patients with thrombocytopenia a...

Facile and Scalable Synthesis of Metal- and Nitrogen-Doped Carbon Nanotubes for Efficient Electrochemical CO Reduction.

Metal- and nitrogen-doped carbon (M-N-C) is a promising material to catalyze electrochemical CO redu...

Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information.

The past two decades have seen a significant increase in the use of CT, with a corresponding rise in...

A convolutional attention mapping deep neural network for classification and localization of cardiomegaly on chest X-rays.

Building a reliable and precise model for disease classification and identifying abnormal sites can ...

Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder.

Intrinsically disordered proteins (IDPs) account for more than 50% of the human proteome and are clo...

Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning.

Attention Deficit Hyperactivity Disorder (ADHD) is a type of mental health disorder that can be seen...

Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.

BACKGROUND: Quantification of chamber size and systolic function is a fundamental component of cardi...

The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective.

In face-to-face interactions, parties rapidly react and adapt to each other's words, movements and e...

Developing an Implementation Model for ADHD Intervention in Community Clinics: Leveraging Artificial Intelligence and Digital Technology.

Implementation of behavior therapy for ADHD faces challenges in community settings. We describe deve...

NLS: An accurate and yet easy-to-interpret prediction method.

Over the last years, the predictive power of supervised machine learning (ML) has undergone impressi...

Design and Performance Verification of a Novel RCM Mechanism for a Minimally Invasive Surgical Robot.

Minimally invasive surgical robots have the advantages of high positioning accuracy, good stability,...

Leukocyte deep learning classification assessment using Shapley additive explanations algorithm.

INTRODUCTION: A peripheral blood smear is a basic test for hematological disease diagnosis. This tes...

Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes.

Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are support...

Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions.

Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions ...

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