Pediatrics

Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.

BACKGROUND: Last-minute surgery cancellation represents a major wastage of resources and can cause significant inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) ...

Machine Learning Applied to Predicting Microorganism Growth Temperatures and Enzyme Catalytic Optima.

Enzymes that catalyze chemical reactions at high temperatures are used for industrial biocatalysis, applications in molecular biology, and as highly evolvable starting points for protein engineering. The optimal growth temperature (OGT) of organisms is commonly used to estimate the stability of enzymes encoded in their genomes, but the number of experimentally determined OGT values are limited, pa...

Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology.

BACKGROUND: Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns a...

Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences.

Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide varie...

Prediction models for high risk of suicide in Korean adolescents using machine learning techniques.

OBJECTIVE: Suicide in adolescents is a major problem worldwide and previous history of suicide ideation and attempt represents the strongest predictor...

Ascending Lipopolysaccharide-Induced Intrauterine Inflammation in Near-Term Rabbits Leading to Newborn Neurobehavioral Deficits.

BACKGROUND: Chorioamnionitis from ascending bacterial infection through the endocervix is a potential risk factor for cerebral palsy. Tetrahydrobiopte...

Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network.

The uncontrollable growth of cells in the breast tissue causes breast cancer which is the second most common type of cancer affecting women in the Uni...

Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data.

The purpose of this study is to develop a machine learning model to categorically classify MR elastography (MRE)-derived liver stiffness using clinic...

Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.

PURPOSE: To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique.

Towards the automated economic assessment of newborn screening for rare diseases.

OBJECTIVE: Economic assessments of newborn screening programs for rare diseases involve the use of models and require huge efforts to synthesize infor...

Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network.

Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in ear...

Ten Ways Artificial Intelligence Will Transform Primary Care.

Artificial intelligence (AI) is poised as a transformational force in healthcare. This paper presents a current environmental scan, through the eyes o...

Artificial Intelligence based facial recognition for Mood Charting among men on life style modification and it's correlation with cortisol.

UNLABELLED: Today, clinicians and researchers believe that mood disorders in children and adolescents remain one of the most under diagnosed mental he...

Nurses' Views on the Potential Use of Robots in the Pediatric Unit.

PURPOSE: To describe nurses' views on the potential use of robots in the pediatric unit.

Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis.

OBJECTIVES: To determine the degree of inter-rater reliability (IRR) between human and artificial intelligence (AI) interpretation of fetal heart rate...

Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis.

BACKGROUND: The aim of this study was to evaluate the hypothesis that a deep convolutional neural network (DCNN) model could facilitate automated Bras...

Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.

BACKGROUND: An automated method for identifying the anatomical region of an image independent of metadata labels could improve radiologist workflow (e...

Effectiveness of ozonated saline in the treatment of VX2 tumors in rabbits.

To investigate the efficacy, safety, and associated mechanisms of injected ozonated saline in the treatment of VX2 tumors. A total of 90 rabbits bea...

Analysis of Unstructured Text-Based Data Using Machine Learning Techniques: The Case of Pediatric Emergency Department Records in Nicaragua.

Free-text information is still widely used in emergency department (ED) records. Machine learning techniques are useful for analyzing narratives, but ...

Deep learning in ophthalmology: The technical and clinical considerations.

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (...