AIMC Topic: Child

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MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.

Scientific reports
Pediatric viral encephalitis is an acute central nervous system infection caused by various viruses, with diverse clinical manifestations and challenges in early diagnosis. The traditional diagnostic methods lack sufficient sensitivity and specificit...

Explainable machine-learning-based predictions of blood lead levels and school drinking water contamination among children: a case study in Washington DC.

Scientific reports
Water quality degradation poses significant risks to human health, ecosystem, and community. Many cities continue to rely on outdated pipes and water distribution networks that are highly susceptible to leaks, corrosion, and lead contamination. The p...

Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.

Scientific reports
This study proposes a novel machine learning (ML)-based stacking technique that integrates Single Nucleotide Polymorphisms (SNPs) and inferred local ancestry (LA) to improve predictive accuracy in clinical outcomes. Asthma, particularly severe asthma...

Distinct neuroimaging subtypes of ADHD among adolescents based on semi-supervised learning.

Translational psychiatry
Attention deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder diagnosed and subtyped solely based on clinical traits, which are prone to subjective judgment and lack of reliability. Also, the clinical subtyping does...

Performance of the pediatric index of mortality (PIM-3) in a Moroccan PICU: challenges in resource-limited settings.

European journal of pediatrics
UNLABELLED: Prognostic scores such as the Pediatric Index of Mortality (PIM-3) are widely used to estimate mortality risk in PICUs, yet their performance in low- and middle-income countries (LMICs) remains uncertain. We aimed to evaluate the predicti...

Large language model as a clinical decision support tool in the initial management of critically ill children: a pilot evaluation.

European journal of pediatrics
UNLABELLED: Large language models (LLMs) like ChatGPT are being explored as clinical decision support tools, but their reliability in pediatric acute care remains uncertain. This pilot study assessed ChatGPT-4.0's performance in the early management ...

Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation.

JMIR medical informatics
BACKGROUND: Missing data are a common challenge in electronic health record (EHR)-based prediction modeling. Traditional imputation methods may not suit prediction or machine learning models, and real-world use requires workflows that are implementab...

Relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis.

eLife
Cognitive abilities are closely tied to mental health from early childhood. This study explores how neurobiological units of analysis of cognitive abilities-multimodal neuroimaging and polygenic scores (PGS)-represent this connection. Using data from...

Machine learning prediction of mortality in pediatric fungemia using the Candida score.

Scientific reports
Pediatric fungemia in pediatric intensive care units (PICUs) carries high mortality. We evaluated whether the Candida Score, combined with clinical variables, predicts mortality after diagnosis using a prespecified multivariable logistic regression (...

Predicting the risk of asthma development in youth using machine learning models.

PloS one
Asthma is a chronic respiratory disease characterized by wheezing and difficulty breathing, which disproportionally affects 4.7 million children in the U.S. Currently, there is a lack of asthma predictive models for youth with good performance. This ...