Pediatrics

ADHD/ADD

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

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Artificial Intelligence Solutions for Analysis of X-ray Images.

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care ...

Investigation of MDMA Inhibitory Effect on CytochromeP450 3A4 in Isolated Perfused Rat Liver Model Using Tramadol.

MDMA (methylenedioxymethamphetamine) is a synthetic compound, which is a structurally derivative of...

A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure.

We created a facet atlas that maps the interrelations between facet scales from 13 hierarchical pers...

Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers.

With the tremendous development of artificial intelligence, many machine learning algorithms have be...

High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks.

Aiming at high-resolution radar target recognition, new convolutional neural networks, namely, Incep...

Identification of competing neural mechanisms underlying positive and negative perceptual hysteresis in the human visual system.

Hysteresis is a well-known phenomenon in physics that relates changes in a system with its prior his...

CAB U-Net: An end-to-end category attention boosting algorithm for segmentation.

With the development of machine learning and artificial intelligence, many convolutional neural netw...

Fully convolutional attention network for biomedical image segmentation.

In this paper, we embed two types of attention modules in the dilated fully convolutional network (F...

Is P&T Ready to Add Rapid Cycle Analytics to Formulary?

The intent of this article is to evaluate a novel approach, using rapid cycle analytics and real wo...

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.

BACKGROUND: Screening for chronic kidney disease is a challenge in community and primary care settin...

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces ...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years ...

Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data.

BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for subst...

Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare.

The objective of this article is to discuss the inherent bias involved with artificial intelligence-...

Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD.

BACKGROUND: Those with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorde...

Unsupervised Machine Learning in Pathology: The Next Frontier.

Applications of artificial intelligence and particularly deep learning to aid pathologists in carryi...

Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelop...

Predicting Transition Words Between Sentence for English and Spanish Medical Text.

Transition words add important information and are useful for increasing text comprehension for read...

Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical...

Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study.

BACKGROUND: Methylphenidate, a stimulant used to treat attention deficit hyperactivity disorder, has...

Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.

BACKGROUND: This study evaluated whether natural language processing (NLP) of psychotherapy note tex...

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