Pediatrics

Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning.

OBJECTIVE: The medical community recently experienced a severe shortage of blood culture media bottles. Rates of blood stream infection (BSI) among critically ill children are low. We sought to design a machine learning (ML) model able to identify children at low risk for BSI to improve blood culture diagnostic stewardship.

Digital Pathology and Artificial Intelligence in Pediatric Pathology.

Applications of artificial intelligence (AI) and machine learning (ML) are rapidly developing to support the diagnosis and classification of pathology specimens. These tools rely on digitization of pathology glass slides as whole slide images, allowing computers to interpret image information. Tools to support the evaluation of pediatric pathology specimens have been slower to develop, in part bec...

Ensemble learning of deep CNN models and two stage level prediction of Cobb angle on surface topography in adolescents with idiopathic scoliosis.

This study employs Convolutional Neural Networks (CNNs) as feature extractors with appended regression layers for the non-invasive prediction of Cobb ...

Using Machine Learning to Identify Predictors of Maternal and Infant Hair Cortisol Concentration Before and During the COVID-19 Pandemic.

Hair cortisol concentration (HCC) has been theorized to reflect chronic stress, and maternal and infant HCC may be correlated due to shared genetic, p...

Addressing Workforce and Ethical Gaps in AI-Driven Mental Health Care: A Response to Higgins and Wilson.

Artificial intelligence (AI)-based clinical decision support systems (CDSS) hold great promise for mental health (MH) care, offering opportunities to ...

Artificial Intelligence as a Language Barrier Application in a Simulated Health Care Setting: A Proof-of-Concept Study.

OBJECTIVE: We evaluated the accuracy of an artificial intelligence program (ChatGPT 4.0) as a medical translation modality in a simulated pediatric ur...

Machine Learning for Clinical Decision Support in the Neonatal Intensive Care Unit.

The neonatal intensive care unit (NICU) is a data-rich environment that is an ideal setting for the implementation of machine learning (ML) and artifi...

Constructing a predictive model of negative academic emotions in high school students based on machine learning methods.

Negative academic emotions reflect the negative experiences that learners encounter during the learning process. This study aims to explore the effect...

Applying Duties to Oversee AI to Medical Practitioners in Australia.

The ethical challenges of artificial intelligence (AI) in health require guardrails to mitigate and address them including: regulation, practice stand...

Artificial Intelligence in the Clinic: Creating Harmony or Just Adding Noise?

Although still limited, the integration of artificial intelligence (AI) in health care has rapidly expanded in the past few years, especially in oncol...

An informed deep learning model of the Omicron wave and the impact of vaccination.

The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and has since evolved into multiple lineages. Understanding its transmission, v...

Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study.

BACKGROUND: Pre-eclampsia (PE) contributes to more than one-fourth of all maternal deaths and half a million newborn deaths worldwide every year. Earl...

Predictors of smartphone addiction in adolescents with depression: combing the machine learning and moderated mediation model approach.

Smartphone addiction (SA) significantly impacts the physical and mental health of adolescents, and can further exacerbate existing mental health issue...

Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.

Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artific...

Comparative analysis of GPT-4 and Google Gemini's consistency with pediatric otolaryngology guidelines.

OBJECTIVE: To evaluate the accuracy and completeness of large language models (LLMs) in interpreting pediatric otolaryngology guidelines.

Machine learning models for predicting malnutrition in NICU patients: A comprehensive benchmarking study.

Malnutrition, affecting both adults and children globally, results from inadequate nutrient intake or loss of body mass. Traditional screening tools, ...

Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors.

BACKGROUND: Neonatal low birth weight (LBW) is a significant predictor of increased morbidity and mortality among newborns. Predominantly, traditional...

Development and validation of an integrated residual-recurrent neural network model for automated heart murmur detection in pediatric populations.

Congenital heart disease affects approximately 1% of children worldwide, with a number of cases in resource-limited settings remaining undiagnosed thr...

Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts

Accurate modeling of subjective phenomena such as emotion expression requires data annotated with authors' intentions. Commonly such data is collect...

Three-dimensional automated segmentation of adolescent idiopathic scoliosis on computed tomography driven by deep learning: A retrospective study.

Accurate vertebrae segmentation is crucial for modern surgical technologies, and deep learning networks provide valuable tools for this task. This stu...