ADHD/ADD

Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia.

Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic agents delivery to these cells remains a challenge. This study investigates modified lipid nanoparticles (LNP) for mRNA delivery to hyperactivated microglia, particularly those with pro-inflammatory characteristics, utilizing supervised machine learning...

Is it necessary? A framework for assessing the utility of A.I. in HRM practices.

Artificial Intelligence is expected to be a value-adding intervention in HRM processes; however, there is still a large gap between its perception of value-addition and its actual utility. In this article, we utilize transaction cost and resource-based views to build a framework to assess the suitability and potential adoption of AI-based tools in specific HRM processes. AI-based tools add value w...

Author name disambiguation based on heterogeneous graph neural network.

With the dramatic increase in the number of published papers and the continuous progress of deep learning technology, the research on name disambiguat...

Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework.

In recent years, the number of people suffering from depression has gradually increased, and early detection is of great significance for the well-bei...

Better off alone? Artificial intelligence can demonstrate superior performance without clinician input.

Recent studies challenge the assumption that human-artificial intelligence (AI) collaboration is universally optimal, highlighting tasks where AI alon...

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learn...

Interpretable machine learning approaches for children's ADHD detection using clinical assessment data: an online web application deployment.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental disorder characterized by hyperactivity, impulsivity, and inattentio...

Carbon source dosage intelligent determination using a multi-feature sensitive back propagation neural network model.

The carbon reduction concept drives the development of low-carbon and sustainable wastewater treatment plant (WWTP) operation technologies. In the den...

Augmented Intelligence-Based Interference Pattern Analysis (AI-IPA) in Concentric Needle Electromyography.

INTRODUCTION/AIMS: To add objectivity to the routine needle electromyography examination, we describe an "Augmented Intelligence" based interference p...

A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD.

BACKGROUND: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children and adolescents characterized by persi...

Differentiating Functional Connectivity Patterns in ADHD and Autism Among the Young People: A Machine Learning Solution.

OBJECTIVE: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elemen...

Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder.

Although the efficacy of pharmacy in the treatment of attention deficit/hyperactivity disorder (ADHD) has been well established, the lack of predictor...

Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration.

The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neu...

Larger models yield better results? Streamlined severity classification of ADHD-related concerns using BERT-based knowledge distillation.

This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT-based model for natural langu...

Machine learning-assisted prediction of engineered carbon systems' capacity to treat textile dyeing wastewater via adsorption technology.

Dyes are widely used in industries like printing, cosmetics, paper, leather processing, textiles, and manufacturing to add color to products. However,...

Detecting noncredible symptomology in ADHD evaluations using machine learning.

INTRODUCTION: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults...

Knowledge-Guided Semantically Consistent Contrastive Learning for sequential recommendation.

Contrastive learning has gained dominance in sequential recommendation due to its ability to derive self-supervised signals for addressing data sparsi...

Sequential recommendation via agent-based irrelevancy skipping.

Sequential Recommendation is based on modelling sequential dependencies in user interactions to produce subsequent recommendation results. However, du...

Contrastive Graph Representation Learning with Adversarial Cross-View Reconstruction and Information Bottleneck.

Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the succes...

Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.

To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-rela...