AIMC Topic: Child

Clear Filters Showing 1591 to 1600 of 3433 articles

Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVES: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithm...

Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-Resolution Electrogastrogram.

IEEE transactions on bio-medical engineering
OBJECTIVE: Pediatric functional nausea is challenging for patients to manage and for clinicians to treat since it lacks objective diagnosis and assessment. A data-driven non-invasive diagnostic screening tool that distinguishes the electro-pathophysi...

An unsupervised machine learning approach to evaluate sports facilities condition in primary school.

PloS one
Sports facilities have been acknowledged as one of the crucial environmental factors for children's physical education, physical fitness, and participation in physical activity. Finding a solution for the effective and objective evaluation of the con...

A Principal Neighborhood Aggregation-Based Graph Convolutional Network for Pneumonia Detection.

Sensors (Basel, Switzerland)
Pneumonia is one of the main causes of child mortality in the world and has been reported by the World Health Organization (WHO) to be the cause of one-third of child deaths in India. Designing an automated classification system to detect pneumonia h...

A Heterogeneous Ensemble Learning Method For Neuroblastoma Survival Prediction.

IEEE journal of biomedical and health informatics
Neuroblastoma is a pediatric cancer with high morbidity and mortality. Accurate survival prediction of patients with neuroblastoma plays an important role in the formulation of treatment plans. In this study, we proposed a heterogeneous ensemble lear...

Incidence and resolution of de novo hydronephrosis after pediatric robot-assisted laparoscopic extravesical ureteral reimplantation for primary vesicoureteral reflux.

Journal of pediatric urology
INTRODUCTION: With the advent of robot-assisted laparoscopic ureteral reimplantation (RALUR) for primary vesicoureteral reflux (VUR), understanding and minimizing its complications continues to be critical. Incidence of de novo hydronephrosis after R...

iCatcher: A neural network approach for automated coding of young children's eye movements.

Infancy : the official journal of the International Society on Infant Studies
Infants' looking behaviors are often used for measuring attention, real-time processing, and learning-often using low-resolution videos. Despite the ubiquity of gaze-related methods in developmental science, current analysis techniques usually involv...

Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.

Sensors (Basel, Switzerland)
Forecasted gait trajectories of children could be used as feedforward input to control lower limb robotic devices, such as exoskeletons and actuated orthotic devices (e.g., Powered Ankle Foot Orthosis-PAFO). Several studies have forecasted healthy ga...

Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model.

Journal of healthcare engineering
In order to compare magnetic resonance imaging (MRI) findings of patients with large vestibular aqueduct syndrome (LVAS) in the stable hearing loss (HL) group and the fluctuating HL group, this paper provides reference for clinicians' early intervent...