AIMC Topic: Child, Preschool

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Rapid identification of COVID-19 severity in CT scans through classification of deep features.

Biomedical engineering online
BACKGROUND: Chest CT is used for the assessment of the severity of patients infected with novel coronavirus 2019 (COVID-19). We collected chest CT scans of 202 patients diagnosed with the COVID-19, and try to develop a rapid, accurate and automatic t...

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.

Sensors (Basel, Switzerland)
Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows compr...

Rapid whole-heart CMR with single volume super-resolution.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed-ups using ...

Self-initiations in young children with autism during Pivotal Response Treatment with and without robot assistance.

Autism : the international journal of research and practice
The initiation of social interaction is often defined as a core deficit of autism spectrum disorder. Optimizing these self-initiations is therefore a key component of Pivotal Response Treatment, an established intervention for children with autism sp...

Robotic approach to the reduction of dental anxiety in children.

Acta odontologica Scandinavica
OBJECTIVE: We introduced a humanoid robot for the use of techno-psychological distraction techniques in children aged 4-10 to reduce their anxiety and improve their behaviour during dental treatment.

Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018.

Scientific reports
Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. Th...

Machine learning-based prediction of persistent oppositional defiant behavior for 5 years.

Nordic journal of psychiatry
BACKGROUND: Early detection of oppositional defiant behavior is warranted for timely intervention in children at risk. This study aimed to build a predictive model of persistent oppositional defiant behavior based on a machine learning algorithm.

Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Clinical decision-making in kidney transplant (KT) during the coronavirus disease 2019 (COVID-19) pandemic is understandably a conundrum: both candidates and recipients may face increased acquisition risks and case fatality rates (CFRs). Given our po...

Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort.

BMC medicine
BACKGROUND: The early life risk factors of childhood obesity among preterm infants are unclear and little is known about the influence of the feeding practices. We aimed to identify early life risk factors for childhood overweight/obesity among prete...

Development and Validation of Forecasting Next Reported Seizure Using e-Diaries.

Annals of neurology
OBJECTIVE: There are no validated methods for predicting the timing of seizures. Using machine learning, we sought to forecast 24-hour risk of self-reported seizure from e-diaries.