ADHD/ADD

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 elements. In this paper, we attempt to differentiate these conditions among the young people in terms of intrinsic patterns of brain connectivity revealed during resting state using machine learning approaches. We had two key objectives: (a) to determine t...

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 predictors of treatment response poses great challenges for personalized treatment. The current study employed a comprehensive approach, combining genome-wide association analyses (GWAS) and deep learning (DL) methods, to elucidate the genetic underpinnings o...

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...

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parc...

A discriminative multi-modal adaptation neural network model for video action recognition.

Research on video-based understanding and learning has attracted widespread interest and has been adopted in various real applications, such as e-heal...

A Longitudinal Prediction of Suicide Attempts in Borderline Personality Disorder: A Machine Learning Study.

Borderline personality disorder (BPD) is associated with a high risk of suicide. Despite several risk factors being known, identifying vulnerable pati...

Analysis of User-Generated Posts on Social Media of Adjuvant Analgesics: A Machine Learning Study.

Antiepileptics and antidepressants are frequently prescribed for chronic pain, but their efficacy and potential adverse effects raise concerns, inclu...

Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.

Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and ef...

Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.

Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significa...

Emotion recognition using multi-scale EEG features through graph convolutional attention network.

Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in pa...

Combining Group Contribution Method and Semisupervised Learning to Build Machine Learning Models for Predicting Hydroxyl Radical Rate Constants of Water Contaminants.

Machine learning is an effective tool for predicting reaction rate constants for many organic compounds with the hydroxyl radical (HO). Previously rep...

Objective approach to diagnosing attention deficit hyperactivity disorder by using pixel subtraction and machine learning classification of outpatient consultation videos.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder, affecting between 5% and 7% of school-a...

Cost-Utility Analysis of Add-on Cannabidiol vs Usual Care Alone for the Treatment of Seizures in Patients With Treatment-Resistant Lennox-Gastaut Syndrome or Dravet Syndrome in the Netherlands.

Lennox-Gastaut syndrome (LGS) and Dravet syndrome (DS) are severe, treatment-refractory, epileptic encephalopathies that often develop in infancy or ...