Pediatrics

Latest AI and machine learning research in pediatrics for healthcare professionals.

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Effect of Cd and Pb Pollutions on Physiological Growth: Wavelet Neural Network (WNN) as a New Approach on Age Determination of Coenobita scaevola.

Environmental pollution of aquatic ecosystems leads to an interference in several fundamental bioche...

Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data.

Mining social media data for studying the human condition has created new and unique challenges. Whe...

Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.

A large recent study analyzed the relationship between multiple factors and neonatal outcome and in...

Adverse Outcomes due to Aggressive Fluid Resuscitation in Children: A Prospective Observational Study.

Fluid management has a major impact on the duration, severity, and outcome of critically ill childre...

Association of Vitamin D and Parathyroid Hormone Levels in Overweight and Obese Adolescents.

Vitamin D deficiency in known to be high in obese and overweight adolescents. Few studies in other c...

Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis.

BACKGROUND: Dry weight is the lowest weight patients on hemodialysis can tolerate; correct dry weigh...

Outcomes of Intradetrusor OnabotulinumtoxinA Injection in Adults with Congenital Spinal Dysraphism in Tertiary Transitional Urology Clinic.

PURPOSE: Published data regarding intradetrusor injection of onabotulinumtoxinA in adults with conge...

A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection.

This study concerns the task of automatic structural heart abnormality risk detection from digital p...

Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection.

The aim of this study was to develop methods for detecting the nonstationary periodic characteristic...

Mapping the transmission risk of Zika virus using machine learning models.

Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public ...

Fetal health status prediction based on maternal clinical history using machine learning techniques.

BACKGROUND AND OBJECTIVE: Congenital anomalies are seen at 1-3% of the population, probabilities of ...

From lexical regularities to axiomatic patterns for the quality assurance of biomedical terminologies and ontologies.

Ontologies and terminologies have been identified as key resources for the achievement of semantic i...

Medical concept normalization in social media posts with recurrent neural networks.

Text mining of scientific libraries and social media has already proven itself as a reliable tool fo...

Reinforced Adversarial Neural Computer for de Novo Molecular Design.

In silico modeling is a crucial milestone in modern drug design and development. Although computer-a...

Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.

The possibility of using the atomic coordinates of protein-ligand complexes to assess binding affini...

Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment.

Cardiotocography (CTG) is applied routinely for fetal monitoring during the perinatal period to decr...

Modeling asynchronous event sequences with RNNs.

Sequences of events have often been modeled with computational techniques, but typical preprocessing...

Female sexual dysfunction in systemic sclerosis: The role of endothelial growth factor and endostatin.

INTRODUCTION: Since female sexual dysfunction in systemic sclerosis women is multifactorial, we can ...

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

BACKGROUND: Central nervous system anomalies represent a wide range of congenital birth defects, wit...

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

Deep neural networks are increasingly being used in both supervised learning for classification task...

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