AIMC Topic: Infant, Newborn

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Detection of pre-ictal epileptic events using a self-attention based neural network from raw Neonatal EEG data.

Computers in biology and medicine
Epileptic seizures can occur unpredictably, making real-time monitoring and early warning systems critical, especially in neonatal patients, where timely intervention can significantly improve outcomes. Neonatal seizures are often subtle and difficul...

Unsupervised single-image super-resolution for infant brain MRI.

NeuroImage
Acquiring high-resolution (HR) MR images of infant brains is challenging due to lengthy scan times and limited subject compliance. Image super-resolution (SR) techniques can generate HR images from low-resolution (LR) inputs, reducing the need for ex...

Development of Machine Learning-Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts.

JMIR public health and surveillance
BACKGROUND: Rapid weight gain (RWG) during infancy, defined as an upward crossing of one centile line on a weight growth chart, is highly predictive of subsequent obesity risk. Identification of infant RWG could facilitate obesity risk assessment fro...

A Machine Learning-Based Clustering Analysis to Explore Bisphenol A and Phthalate Exposure from Medical Devices in Infants with Congenital Heart Defects.

Environmental health perspectives
BACKGROUND: Plastic-containing medical devices are commonly used in critical care units and other patient care settings. Patients are often exposed to xenobiotic agents that are leached out from plastic-containing medical devices, including bisphenol...

Identifying determinants of under-5 mortality in Bangladesh: A machine learning approach with BDHS 2022 data.

PloS one
BACKGROUND: Under-5 mortality in Bangladesh remains a critical indicator of public health and socio-economic development. Traditional methods often struggle to capture the complex, non-linear relationships influencing under-5 mortality. This study le...

Undesired nexus poor health status of child under-five: A case study of Pakistan.

PloS one
Childhood morbidity and mortality are key indicators of human development, particularly reflecting poor health conditions in children. In Pakistan, child mortality remains a serious problem despite efforts to reduce it. One factor that may be associa...

Development of a machine learning-based predictive nomogram for screening children with juvenile idiopathic arthritis: a pseudo-longitudinal study of 223,195 children in the United States.

Frontiers in public health
BACKGROUND: Juvenile idiopathic arthritis (JIA) is a prevalent chronic rheumatological condition in children, with reported prevalence ranging from 12. 8 to 45 per 100,000 and incidence rates from 7.8 to 8.3 per 100,000 person-years. The diagnosis of...

A pediatric ECG database with disease diagnosis covering 11643 children.

Scientific data
Electrocardiogram (ECG) is a common non-invasive diagnostic tool for cardiovascular diseases. Adequate data is crucial in utilizing deep learning to achieve intelligent diagnosis of ECG. The existing ECG datasets almost only focus on adults and most ...

Sex-related differences and associated transcriptional signatures in the brain ventricular system and cerebrospinal fluid development in full-term neonates.

Biology of sex differences
BACKGROUND: The cerebrospinal fluid (CSF) is known to serve as a unique environment for neurodevelopment, with specific proteins secreted by epithelial cells of the choroid plexus (CP) playing crucial roles in cortical development and cell differenti...

Novel Technologies in Preterm Birth Prediction: Current Advances and Ethical Challenges.

Journal of mother and child
Preterm birth (PTB) remains a significant challenge in modern obstetric practice, posing considerable risks to maternal and neonatal health. Despite advancements in medical technology, the incidence of PTB remains high, and its prediction continues t...