Pediatrics

Deep learning predicts extreme preterm birth from electronic health records.

OBJECTIVE: Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the majority of newborn deaths. We investigated the extent to which deep learning models that consider temporal relations documented in electronic health r...

A Research Roadmap: Connected Health as an Enabler of Cancer Patient Support.

The evidence that quality of life is a positive variable for the survival of cancer patients has prompted the interest of the health and pharmaceutical industry in considering that variable as a final clinical outcome. Sustained improvements in cancer care in recent years have resulted in increased numbers of people living with and beyond cancer, with increased attention being placed on improving ...

Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis.

The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonat...

Development and validation of deep learning algorithms for scoliosis screening using back images.

Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5-5.2% worldwide. The traditional methods for...

Developments of reproductive management and biotechnology in the pig.

This review aims to describe changes in production environment, management tools and technology to alleviate problems seen with the present hyperproli...

Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring.

BACKGROUND AND OBJECTIVES: Intrauterine Growth Restriction (IUGR) is a fetal condition defined as the abnormal rate of fetal growth. The pathology is ...

Novel Screening Method Identifies PI3Kα, mTOR, and IGF1R as Key Kinases Regulating Cardiomyocyte Survival.

Background Small molecule kinase inhibitors (KIs) are a class of agents currently used for treatment of various cancers. Unfortunately, treatment of c...

Artificial intelligence applications for pediatric oncology imaging.

Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of...

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calcul...

Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction.

Neuroimaging-based infant age prediction is important for brain development analysis but often suffers insufficient data. To address this challenge, w...

Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning.

Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high...

Machine Learning Approaches to Improve Three Basic Plant Phenotyping Tasks Using Three-Dimensional Point Clouds.

Developing automated methods to efficiently process large volumes of point cloud data remains a challenge for three-dimensional (3D) plant phenotyping...

Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

BACKGROUND: To characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a...

A deep learning model for pediatric patient risk stratification.

OBJECTIVES: Current models for patient risk prediction rely on practitioner expertise and domain knowledge. This study presents a deep learning model-...

Endocan serum concentration in uninfected newborn infants.

INTRODUCTION: Endocan is a specific endothelial mediator involved in the inflammatory response. Its role in the diagnosis of sepsis has been studied i...

Self Multi-Head Attention-based Convolutional Neural Networks for fake news detection.

With the rapid development of the internet, social media has become an essential tool for getting information, and attracted a large number of people ...

Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data.

Prognostic tumor growth modeling via volumetric medical imaging observations can potentially lead to better outcomes of tumor treatment management and...

Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach.

Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but con...

Laparoscopic resection of choledochal cysts in adults: a series from Turkey.

OBJECTIVES: Choledochal cyst is a congenital disease in which surgical treatment is preliminary because of the potential for malignancy. In recent yea...

[E-health and "Cancer outside the hospital walls", Big Data and artificial intelligence].

To heal otherwise in oncology has become an imperative of Public Health and an economic imperative in France. Patients can therefore receive live most...