Pediatrics

Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic.

Patient "no-shows" are missed appointments resulting in clinical inefficiencies, revenue loss, and d...

A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis.

UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical de...

MVP predicts the pathogenicity of missense variants by deep learning.

Accurate pathogenicity prediction of missense variants is critically important in genetic studies an...

Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study.

OBJECTIVES: This study investigated the usefulness and performance of a two-stage attention-aware co...

Human-scale Brain Simulation via Supercomputer: A Case Study on the Cerebellum.

Performance of supercomputers has been steadily and exponentially increasing for the past 20 years, ...

Identifying intentional injuries among children and adolescents based on Machine Learning.

BACKGROUND: Compared to other studies, the injury monitoring of Chinese children and adolescents has...

MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors.

Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers fo...

Development and validation of a deep-learning-based pediatric early warning system: A single-center study.

BACKGROUND: Early detection and prompt intervention for clinically deteriorating events are needed t...

A new approach to modelling in adult congenital heart disease: artificial intelligence.

Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.

Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In thi...

Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission.

The aim of this study was to develop a predictive model of pediatric mortality in the early stages o...

A deep learning framework for F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.

PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups ...

Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State Functional Magnetic Resonance Imaging.

OBJECTIVE: This study aims to evaluate the performance of convolutional neural networks (CNNs) train...

A Novel Machine Learning Framework for Comparison of Viral COVID-19-Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis.

BACKGROUND: Social media plays a critical role in health communications, especially during global he...

AI for Improving Children's Health: A Community Case Study.

The Indian health care system lacks the infrastructure to meet the health care demands of the countr...

Early response to SPN-812 (viloxazine extended-release) can predict efficacy outcome in pediatric subjects with ADHD: a machine learning post-hoc analysis of four randomized clinical trials.

Machine learning (ML) was used to determine whether early response can predict efficacy outcome in p...

The impact of errors in infant development: Falling like a baby.

What is the role of errors in infants' acquisition of basic skills such as walking, skills that requ...

Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy.

The development of artificial intelligence (AI) in medicine raises fundamental ethical issues. As on...

Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot.

Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric ana...

The diagnosis of tuberculous meningitis in adults and adolescents: protocol for a systematic review and individual patient data meta-analysis to inform a multivariable prediction model.

Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnos...

Integrated meta-analysis and machine learning approach identifies acyl-CoA thioesterase with other novel genes responsible for biofilm development in Staphylococcus aureus.

Biofilm forming Staphylococcus aureus is a major threat to the health-care industry. It is important...