ADHD/ADD

Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.

OBJECTIVE: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annotated data available for training. Convolutional neural networks (CNNs) have the potential to improve image analysis and have demonstrated good perform...

BMIVPOT, a Fully Automated Version of the Intravenous Pole: Simulation, Design, and Evaluation.

Robotic intravenous poles are automated supportive instrument that needs to be triggered by patients to hold medications and needed supplies. Healthcare engineering of robotic intravenous poles is advancing in order to improve the quality of health services to patients worldwide. Existing intravenous poles in the market were supportive to patients, yet they constrained their movement, consumed the...

Artificial Intelligence Solutions for Analysis of X-ray Images.

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient 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 amphetamine. Also, it acts like an amphetamine, s...

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 personality inventories to provide a practically usefu...

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 been applied to the diagnosis of human cancers. Rece...

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

Aiming at high-resolution radar target recognition, new convolutional neural networks, namely, Inception-based VGG (IVGG) networks, are proposed to cl...

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 history. It is also part of human visual experience (...

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 networks (CNNs) based segmentation methods have been p...

Fully convolutional attention network for biomedical image segmentation.

In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks e...

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 world evidence, to optimize and improve the medicati...

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 settings, even in high-income countries. We developed an...

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 collected from surface recordings over the heart. ...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognit...

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 substance use disorders (SUDs). Early identification of...

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-based decision support systems for healthcare. In ...

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 disorder (ADHD) exhibit symptoms of hyperactivity and ina...

Unsupervised Machine Learning in Pathology: The Next Frontier.

Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopat...

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 neurodevelopmental disorder, which is diagnosed using subjecti...

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

Transition words add important information and are useful for increasing text comprehension for readers. Our goal is to automatically detect transitio...