ADHD/ADD

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 artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images, which could add to existing chronic kidney disease screening strategies.

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. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage-time traces. By using ECGs collected over a 34-year period in a large regional health system, we traine...

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...

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 and social burdens every year. Regretfully, a rep...

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 the potential to be used nonmedically, such as fo...

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 text provides additional accuracy over and above curr...

On the localness modeling for the self-attention based end-to-end speech synthesis.

Attention based end-to-end speech synthesis achieves better performance in both prosody and quality compared to the conventional "front-end"-"back-end...

Rolling Locomotion of Cable-Driven Soft Spherical Tensegrity Robots.

Soft spherical tensegrity robots are novel steerable mobile robotic platforms that are compliant, lightweight, and robust. The geometry of these robot...

DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI.

BACKGROUND: Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of brain disorders. Attention...

Assessing Risk Among Correctional Community Probation Populations: Predicting Reoffense With Mobile Neurocognitive Assessment Software.

We seek to address current limitations of forensic risk assessments by introducing the first mobile, self-scoring, risk assessment software that relie...

Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits.

Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communic...

ADHD classification by dual subspace learning using resting-state functional connectivity.

As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studi...

How will digitalisation affect patient treatment in arthroplasty? Part I: Intraoperative aspects.

We cannot deny that technology has become an important part of our daily life, not only in automobiles, houses or cellphones but also in hospitals. In...

Characterization of the complete chloroplast genome of (Cupressaceae), an herb to treat lumbar tuberculosis in China.

belongs to the family Cupressaceae that the branches and leaves is an important Traditional Chinese Medicine in China. In this article, the complete ...