Pediatrics

Development and Validation of an Explainable Prediction Model for Postoperative Recurrence in Pediatric Chronic Rhinosinusitis.

OBJECTIVE: This study aims to develop an interpretable machine learning (ML) predictive model to assess its efficacy in predicting postoperative recurrence in pediatric chronic rhinosinusitis (CRS).

Does smart city construction promote urban green development? Evidence from a double machine learning model.

The issue of whether smart city construction (SCC) can promote urban green development (UGD) is controversial. To address this problem, first, a UGD evaluation index system with four dimensions, namely, green production, green living, green growth, and green ecology, is developed in this study to measure the UGD level. Second, the causal relationship between SCC and UGD is examined by using a doub...

Policy brief: Improving national vaccination decision-making through data.

Life course immunisation looks at the broad value of vaccination across multiple generations, calling for more data power, collaboration, and multi-di...

Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitiv...

Rapid and high accuracy identification of culture medium by CNN of Raman spectra.

Culture media are widely used for biological research and production. It is essential for the growth of microorganisms, cells, or tissues. It includes...

Development of a machine learning model for prediction of intraventricular hemorrhage in premature neonates.

PURPOSE: Intraventricular hemorrhage (IVH) is a common and severe complication in premature neonates, leading to long-term neurological impairments. E...

Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics.

Microplastics (MPs), the plastic debris smaller than 5 mm, are ubiquitous in waterbodies and have been shown to be toxic to aquatic organisms, especia...

Artificial intelligence in the care of children and adolescents with chronic diseases: a systematic review.

UNLABELLED: The integration of artificial intelligence (AI) and machine learning (ML) has shown potential for various applications in the medical fiel...

Age-Related Cognitive and Volumetric Changes in the Brain of African Grasscutter ( (Temminck, 1827)).

The African grasscutter (AGC) () is the second largest rodent in sub-Saharan Africa. It is bred for its organoleptic and culinary properties but also ...

Onset and long-term duration of immunity provided by a single vaccination with recombinant a Marek's disease virus with REV-LTR insertion.

Marek's Disease (MD), caused by Marek's disease virus (MDV), is a highly contagious lymphoproliferative disease in poultry. Despite the fact that MD h...

Artificial intelligence in emergency neuroradiology: Current applications and perspectives.

Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brai...

Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans.

Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized...

Active learning for extracting rare adverse events from electronic health records: A study in pediatric cardiology.

OBJECTIVE: Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization.

Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.

BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and ex...

Early specialization in formative basketball: A machine learning analysis of shooting patterns in U14 and professional players.

Growing evidence supports that early sport specialization in children and adolescents may compromise long-term athlete development and high-performanc...

Evaluating generative artificial intelligence's limitations in health policy identification and interpretation.

Policy epidemiology utilizes human subject-matter experts (SMEs) to systematically surface, analyze, and categorize legally-enforceable policies. The ...

Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.

BACKGROUND: Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, ...

Effects of endophytes on early growth and ascorbate metabolism in .

Understanding the early interactions between plants and endophytes will contribute to a more systematic approach to enhancing endophyte-mediated effec...

Quantitative fibrosis identifies biliary tract involvement and is associated with outcomes in pediatric autoimmune liver disease.

BACKGROUND: Children with autoimmune liver disease (AILD) may develop fibrosis-related complications necessitating a liver transplant. We hypothesize ...

Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.

BACKGROUND: Real-time monitoring of pediatric epileptic seizures poses a significant challenge in clinical practice. In recent years, machine learning...