AIMC Topic: Infant, Newborn

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Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection.

International journal of neural systems
The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time-freque...

A supervised learning approach for diffusion MRI quality control with minimal training data.

NeuroImage
Quality control (QC) is a fundamental component of any study. Diffusion MRI has unique challenges that make manual QC particularly difficult, including a greater number of artefacts than other MR modalities and a greater volume of data. The gold stan...

A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND: Central nervous system anomalies represent a wide range of congenital birth defects, with an incidence of approximately 1% of all births. They are currently diagnosed using ultrasound evaluation. However, there is strong need for a more a...

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

NeuroImage
Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuro...

Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit.

Journal of child neurology
The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine ...

Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.

Autism research : official journal of the International Society for Autism Research
Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrospectively analyzed diagnostic codes associated with de-identified electronic health records using a m...

Lung sounds classification using convolutional neural networks.

Artificial intelligence in medicine
Lung sounds convey relevant information related to pulmonary disorders, and to evaluate patients with pulmonary conditions, the physician or the doctor uses the traditional auscultation technique. However, this technique suffers from limitations. For...

Assessing patient risk of central line-associated bacteremia via machine learning.

American journal of infection control
BACKGROUND: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLAB...

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

International journal of neural systems
Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not ...