Pediatrics

Normative growth trajectories of fetal brain regions validated by satisfactory maturation of neurodevelopmental domains at 2 years of age.

We previously constructed a qualitative, 3D ultrasound derived atlas of the normative spatiotemporal dynamics of fetal brain maturation. Here, using the same healthy multi-national cohort, we applied deep learning methods to 4205 fetal brain scans from 18-27 weeks' gestation, to produce an extensive, quantitative description of the growth of 16 fetal brain structures associated with satisfactory d...

Comparative machine learning models for early prediction of preterm birth from maternal serum biomarkers.

BACKGROUND: Preterm birth (PTB) is a major cause of neonatal morbidity and mortality. Inflammation and metabolic disruption are involved in its pathology. This study aimed to assess maternal serum inflammatory and lipid markers as predictors of PTB using various machine learning models. METHODS: Women who were pregnant and attending antenatal clinics were recruited for this study. A group of 186 f...

Classification of knowledge of fertility period among adolescent girls in East Africa from 2012 to 2022: Machine learning algorithm.

Understanding the time of the menstrual cycle would help women to avoid getting pregnant without the need for surgical, hormonal, or mechanical contra...

Implications of cytokines in Memory B cell for risk stratification and therapeutic framework construction among Pediatric Influenza patents: insights from machine learning and multi-omics.

OBJECTIVE: Cytokine storm and dysregulation of Memory B cell aggravate the symptoms and weaken the therapeutic effectiveness for Pediatric Influenza(P...

Artificial Intelligence-Based Analysis of Central Nervous System Vasculopathy in Pediatric Sickle Cell Anemia.

In children with sickle cell anemia (SCA), central nervous system (CNS) complications such as chronic vasculopathy, silent cerebral infarcts, and over...

Integrative Molecular Pattern Learning for Mental Disorders Via Dual-Effect Matrix-Enabled Multiomics Platform.

Mental health issues are surging in contemporary society, especially among adolescents and adults, harming well-being and social functioning. Objectiv...

Machine learning-assisted HS-GC-IMS for discrimination and traceability of baby bottles based on volatile fingerprints.

Ensuring the safety of food contact materials, particularly baby bottles, is crucial for infant health. In this study, a comprehensive analytical stra...

Oral Nicotine Product Use and E-Cigarette Use Persistence in Adolescents and Young Adults: An Analysis Using Augmented Inverse Probability Weighting.

The impact of oral nicotine product (ONP) use on e-cigarette use persistence among adolescents and young adults (AYA) remains unclear. We applied augm...

Risk Modeling and Association Pathways Integrating Psychosocial Factors for Drug Misuse in Adolescents and Young Adults: A Machine Learning Approach.

INTRODUCTION: Early identification for preventing drug misuse among adolescents and young adults (AYAs) is more cost-effective than drug treatment. Ho...

Machine learning-based prediction of disability in subacute low back pain: a primary care study on clinical and psychosocial determinants.

BACKGROUND: Subacute low back pain (LBP) is a highly prevalent condition and a major contributor to disability and health care burden. Early identific...

KD-SqueezeNet: an efficient deep learning strategy for the multi-task diagnosis of neonatal lung diseases.

BACKGROUND: The integration of deep learning in medical imaging has reached proficiency levels akin to expert clinicians, particularly in tasks requir...

Identifying microbial protease allergens through protein language model-guided homology.

Emerging research links the gut, skin, and oral microbiomes to allergies, with serine proteases (SPs) identified as potential allergens. This study le...

Performance of Large Language Models Under Input Variability in Health Care Applications: Dataset Development and Experimental Evaluation.

BACKGROUND: Large language models (LLMs) are increasingly integrated into health care, where they contribute to patient care, administrative efficienc...

Experiences With Integrating Medical Terminologies Into User Interfaces for a Decision Support System for Primary Care: Conceptual and Development Study.

BACKGROUND: Clinical decision support systems (CDSSs) have shown promise in improving diagnosis in primary care, particularly for chronic diseases. Th...

A comprehensive UK crop yield dataset incorporating satellite, weather, and soil type information.

Agricultural research increasingly relies on data-driven approaches for crop yield prediction that complement more established crop growth models, inc...

Machine Learning Prediction of Recurrence in Pediatric Thyroid Cancer: Malignant Endocrine Tumors Cohort Analysis Using XGBoost and SHAP.

CONTEXT: Pediatric differentiated thyroid carcinoma (DTC) often presents with advanced disease but generally has excellent long-term survival. However...

Modelling Follicular Growth During Ovarian Stimulation Using Agent-based Artificial Intelligence.

CONTEXT: Ovarian stimulation is a key step in medically assisted reproduction (MAR), whereby supraphysiological doses of FSH extend the "FSH window" a...

Prediction of COVID-19 hospitalisation, ICU admission or death following ChAdOx1 vaccination using artificial intelligence: A clinical predictive model from the English RAVEN study.

OBJECTIVES: This study identifies predictors of severe COVID-19 following completion of two-dose primary series of the AZD1222 COVID-19 vaccine, emplo...

Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy.

BACKGROUND: Ketogenic diet therapy (KDT) is an established treatment for drug-resistant epilepsy (DRE); however, methods for predicting its effectiven...

Active surveillance methods to identify adverse events of special interest (AESIs) following vaccination against pandemic diseases: A scoping review.

INTRODUCTION: Active post-vaccination surveillance is vital for ensuring vaccine safety, particularly in monitoring Adverse Events of Special Interest...