Pediatrics

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 fractured and finely layered geological media. However, many inversion methods rely on complex physical models with initial assumptions, leading to non-reproducible estimates and subjective fracture interpretation. To address these limitations, this study utilizes machine learning methods: support ...

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 units, have an important impact on the developing neonatal gut microbiota. Therefore, comprehending the effects of commonly used antibiotic therapy on the gut microbiota and butyrate-producers in early infants could provide information for therapeutic decision-making in the NICU.

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

Adolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, including Generalized Anxiety Disorder (GAD), bein...

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 6-month risk for diabetic ketoacidosis (DKA)-rela...

Classification of α-thalassemia data using machine learning models.

BACKGROUND: Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis i...

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 research in pediatric dentistry is scarce. To crea...

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 learning models and computational techniques, addr...

Intelligent measurement of adolescent idiopathic scoliosis x-ray coronal imaging parameters based on VB-Net neural network: a retrospective analysis of 2092 cases.

BACKGROUND: Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional deformity, and up to now, there has been no literature reporting the ...

Bias in adjudication: Investigating the impact of artificial intelligence, media, financial and legal institutions in pursuit of social justice.

The latest global progress report highlights numerous challenges in achieving justice goals, with bias in artificial intelligence (AI) emerging as a s...

Non-invasive ML methods for diagnosis of congenital heart disease associated with pulmonary arterial hypertension.

OBJECTIVE: Congenital heart disease with pulmonary arterial hypertension (CHD-PAH), caused by CHD, is associated with high clinical mortality. Hence, ...

How do neighborhood environments impact adolescent health: a comprehensive study from subjective and objective perspectives using machine learning method.

Existing studies have established a linear relationship between urban environments and adolescent health, but the combined impacts of subjective and o...

Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outc...

Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.

INTRODUCTION: Pediatric Crohn's disease (CD) easily progresses to an active disease compared with adult CD, making it important to predict and minimiz...

Machine Learning-Based Pathomics Model Predicts Angiopoietin-2 Expression and Prognosis in Hepatocellular Carcinoma.

Angiopoietin-2 (ANGPT2) shows promise as prognostic marker and therapeutic target in hepatocellular carcinoma (HCC). However, assessing ANGPT2 express...

Forecasting Pediatric Trauma Volumes: Insights From a Retrospective Study Using Machine Learning.

INTRODUCTION: Rising pediatric firearm-related fatalities in the United States strain Trauma Centers. Predicting trauma volume could improve resource ...

The Utilization of Natural Language Processing for Analyzing Social Media Data in Nursing Research: A Scoping Review.

This scoping review aimed to identify and synthesize the evidence in existing nursing studies that used natural language processing to analyze social...

From Outbreaks to Artificial Intelligence: A Comprehensive Review of Monkeypox Virus Epidemiology, Diagnosis, Treatment, Vaccination, and Deep Learning Applications.

After the global impact of the COVID-19 pandemic, concerns over virus transmission have risen. A state of health emergency was declared in 2022 due t...

Decoding of pain during heel lancing in human neonates with EEG signal and machine learning approach.

Currently, pain assessment using electroencephalogram signals and machine learning methods in clinical studies is of great importance, especially for ...

Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.

The purpose of this study was to evaluate whether the optimal operating points of adult-oriented artificial intelligence (AI) software differ for pedi...

Effects of immersion bathing in Lactobacillus plantarum CLY-05 on the growth performance, non-specific immune enzyme activities and gut microbiota of Apostichopus japonicus.

In order to study the optimal use of Lactobacillus plantarum in sea cucumber (Apostichopus japonicus), 49 days feeding trial was conducted to determin...