Pediatrics

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

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An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study.

BACKGROUND: Accurate classification of patient complaints is crucial for enhancing patient satisfact...

Semantic segmentation using synthetic images of underwater marine-growth.

INTRODUCTION: Subsea applications recently received increasing attention due to the global expansion...

Longitudinal twin growth discordance patterns and adverse perinatal outcomes.

BACKGROUND: Growth discordance in twin pregnancies is associated with increased perinatal morbidity ...

Evaluation of Generative Artificial Intelligence Models in Predicting Pediatric Emergency Severity Index Levels.

OBJECTIVE: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) ...

A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic ana...

Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation.

Artificial intelligence and robotics are revolutionizing surgical practices by enhancing precision, ...

Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.

Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without p...

Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine.

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolut...

A bioinspired fish fin webbing for proprioceptive feedback.

The propulsive fins of ray-finned fish are used for large scale locomotion and fine maneuvering, yet...

Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield.

Estimating seismic anisotropy parameters, such as Thomson's parameters, is crucial for investigating...

Fine-Grained Fidgety Movement Classification Using Active Learning.

Typically developing infants, between the corrected age of 9-20 weeks, produce fidgety movements. Th...

Effects of feeding of vitamin C on embryonic development, hatching process, and chick rectal temperature of broiler embryos.

Maternal nutritional status plays a crucial role in embryonic development and has persistent effects...

Global research trends in the application of artificial intelligence in oncology care: a bibliometric study.

OBJECTIVE: To use bibliometric methods to analyze the prospects and development trends of artificial...

Key risk factors of generalized anxiety disorder in adolescents: machine learning study.

Adolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, ...

Effects of antibiotic therapy on the early development of gut microbiota and butyrate-producers in early infants.

BACKGROUND: Antibiotics, as the most commonly prescribed class of drugs in neonatal intensive care u...

Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.

To use electronic health record (EHR) data to develop a scalable and transferrable model to predict...

Classification of α-thalassemia data using machine learning models.

BACKGROUND: Around 7% of the global population has congenital hemoglobin disorders, with over 300,00...

Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study.

OBJECTIVES: Approximal caries diagnosis in children is difficult, and artificial intelligence-based ...

Deep learning in 3D cardiac reconstruction: a systematic review of methodologies and dataset.

This study presents an advanced methodology for 3D heart reconstruction using a combination of deep ...

Personalized stress optimization intervention to reduce adolescents' anxiety: A randomized controlled trial leveraging machine learning.

Anxiety symptoms are among the most prevalent mental health disorders in adolescents, highlighting t...

Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound.

PURPOSE: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS cla...

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