Pediatrics

Supervised machine learning models for predicting sepsis-associated acute kidney injury in children: a real-world evaluation.

BACKGROUND: Sepsis-associated acute kidney injury (S-AKI) substantially increases mortality. The recent Phoenix criteria have redefined pediatric sepsis, yet AKI risk factors under this framework remain unclear. This study aimed to develop a machine learning (ML) model to identify key predictors of AKI in pediatric patients with sepsis as defined by the Phoenix criteria. METHODS: This retrospectiv...

Development and validation of an explainable machine learning model for predicting surgical intervention in pediatric intestinal obstruction.

Pediatric intestinal obstruction, a critical acute abdomen condition, carries a risk of intestinal necrosis. Decisions for urgent surgery lack standardized criteria. This study aimed to develop an explainable machine learning (ML) model to predict surgical indication, reducing diagnostic variability. A retrospective study included 642 children from our center (split into training/internal validati...

Growth Hormone Treatment Response and Machine Learning-Based Prediction in Idiopathic GHD and ISS: Analysis of the Korean LG Growth Study.

OBJECTIVE: Individual responses to recombinant human growth hormone (rhGH) therapy vary widely among children with idiopathic growth hormone deficienc...

Explainable TabNet for gestational diabetes prediction with physician-in-the-loop and multi-site clinical validation.

BACKGROUND: Gestational diabetes mellitus (GDM) affects 15-25% of pregnancies worldwide and poses serious risks of macrosomia, preeclampsia, neonatal ...

Early laboratory-based differentiation of severe influenza pneumonia and influenza-associated encephalopathy in children using a multi-model, stability-aware machine-learning workflow: a single-center retrospective cohort study.

BACKGROUND: Influenza-associated encephalopathy and acute necrotizing encephalopathy (ANE) are rare but devastating complications of pediatric influen...

One year quality of life outcomes in critically ill children: a multicenter prospective cohort study.

BACKGROUND: Survivors of pediatric intensive care often experience prolonged morbidity, but recovery trajectories and features associated with impairm...

The development of FEDUPP: feeding experimentation device users processing package to assess learning and cognitive flexibility.

Cognitive flexibility, the ability to adapt behavior in response to changing contingencies, is a key component of adaptive decision-making and is impa...

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines.

BACKGROUND: Complication risks in children and adolescents with type 1 diabetes (T1D) can lead to serious health outcomes if not detected early. Despi...

Large Language Models and Their Applications in Mental Health: Scoping Review.

BACKGROUND: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decis...

Admission blood pressure percentiles and in-hospital mortality in pediatric heart failure: a multicenter retrospective cohort study.

BACKGROUND: Pediatric heart failure (PHF) carries a high mortality burden, yet the prognostic value of admission blood pressure (BP) remains poorly de...

Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery.

Pediatric neurosurgery increasingly utilizes precision medicine, but practitioners encounter challenges in translating complex data into individualize...

Development and nested cross-validation of machine learning models for predicting transfusion requirements in pediatric craniosynostosis surgery.

Blood transfusion is common during pediatric craniosynostosis surgery; however, transfusion volumes and use of cell salvage systems can vary considera...

Hematopoietic Stem Cell Transplant and Brain Volume Changes in Adults With Sickle Cell Disease.

BACKGROUND AND OBJECTIVES: Adults with sickle cell disease (SCD) are at risk of decline in brain health and cognition, even without clinical stroke. S...

A High-Speed Image AI Facilitating the Visual Assessment of the Membrane's Motion in EXCOR VAD.

BACKGROUND: Visual assessment of membrane motion is essential for managing EXCOR VAD, but accuracy depends on observer experience. We evaluated a high...

Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study.

OBJECTIVE: To explore clinically meaningful phenotypes of preeclampsia using unsupervised machine learning. DESIGN: Prospective cohort study. SETTING:...

The dynamics of hepatitis B transmission with treatment and vaccination: A case study in the Ili Region of Xinjiang, China, by a physics-informed neural networks approach.

Hepatitis B virus (HBV) infection remains a leading cause of cirrhosis and hepatocellular carcinoma worldwide, and long-term control relies on sustain...

An interpretable machine learning model for predicting emergence agitation in children: a multicenter development and validation study.

BACKGROUND: Postoperative agitation (EA) is a common complication in pediatric patients, and its early identification is crucial for improving periope...

Identifying significant environmental predictors of change in delay discounting among adolescents in the Adolescent Brain Cognitive Development Study.

Delay discounting (a preference for smaller immediate rewards vs. larger delayed rewards; DD) is a transdiagnostic risk factor for health outcomes inc...

Machine learning-guided risk stratification for Long QT Syndrome genetic variants with hiPSC-derived cardiomyocytes.

AIMS: Long QT syndrome (LQTS) is a life-threatening genetic disorder characterized by prolonged QT intervals on electrocardiograms. Congenital forms a...

Use of Commercially Available Large Language Models to Generate Information Leaflets on Post-Intensive Care Syndrome: Clinical Utility Assessment.

BACKGROUND: Patients and their families without medical knowledge may find professional health care information difficult to understand. The use of la...