Pediatrics

Using Deep Learning for Individual-Level Predictions of Adherence with Growth Hormone Therapy.

The problem of consistent therapy adherence is a current challenge for health informatics, and its s...

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can re...

Use of Artificial Intelligence in Dentistry: Current Clinical Trends and Research Advances.

The field of artificial intelligence (AI) has experienced spectacular development and growth over th...

[Application of the artificial intelligence-rapid whole-genome sequencing diagnostic system in the neonatal/pediatric intensive care unit].

Pediatric patients in the neonatal intensive care unit (NICU) and the pediatric intensive care unit ...

Deep learning for detecting congenital heart disease in the fetus.

Development and validation of a machine learning model to predict mortality risk in patients with COVID-19.

New York City quickly became an epicentre of the COVID-19 pandemic. An ability to triage patients wa...

Machine Learning Predicts the 3D Outcomes of Adolescent Idiopathic Scoliosis Surgery Using Patient-Surgeon Specific Parameters.

STUDY DESIGN: Retrospective descriptive, multicenter study.

Artificial Intelligence in Spine Care.

Artificial intelligence is an exciting and growing field in medicine to assist in the proper diagnos...

[Current status and trends of pediatric pathology based on artificial intelligence analysis].

Brain views that benefit from three-dimensional ultrasound.

PURPOSE OF REVIEW: Fetal central nervous system malformations are among the most common congenital a...

Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model.

OBJECTIVES: To create a machine-learning model identifying potentially avoidable blood draws for ser...

Predicting outcomes in central venous catheter salvage in pediatric central line-associated bloodstream infection.

OBJECTIVE: Central line-associated bloodstream infections (CLABSIs) are a common, costly, and hazard...

Swarming behavior and in vivo monitoring of enzymatic nanomotors within the bladder.

Enzyme-powered nanomotors are an exciting technology for biomedical applications due to their abilit...

Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges.

The advent of large-scale high-performance computing has allowed the development of machine-learning...

AI-Driven Growth of DSOs.

DSOs have been at the forefront of adopting technology that demonstrates value-add to their business...

Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.

OBJECTIVES: Childhood blindness from retinopathy of prematurity (ROP) is increasing as a result of i...

Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support.

OBJECTIVE: Like most real-world data, electronic health record (EHR)-derived data from oncology pati...

Clinical applications of artificial intelligence and machine learning-based methods in inflammatory bowel disease.

Our objective was to review and exemplify how selected applications of artificial intelligence (AI) ...

TL;DR: how well do machines summarize our work?

Development of a predictive model for retention in HIV care using natural language processing of clinical notes.

OBJECTIVE: Adherence to a treatment plan from HIV-positive patients is necessary to decrease their m...

The Human Phenotype Ontology in 2021.

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehens...