AIMC Topic: Child, Preschool

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Population-Driven Synthesis of Personalized Cranial Development From Cross-Sectional Pediatric CT Images.

IEEE transactions on bio-medical engineering
OBJECTIVE: Predicting normative pediatric growth is crucial to identify developmental anomalies. While traditional statistical and computational methods have shown promising results predicting personalized development, they either rely on statistical...

Machine learning methods for sex estimation of sub-adults using cranial computed tomography images.

Anthropologischer Anzeiger; Bericht uber die biologisch-anthropologische Literatur
This research aimed to compare the classification accuracy of three machine learning (ML) methods (random forest (RF), support vector machines (SVM), linear discriminant analysis (LDA)) for sex estimation of sub-adults using cranial computed tomograp...

Algorithmic Fairness in Machine Learning Prediction of Autism Using Electronic Health Records.

Studies in health technology and informatics
Efforts to improve early diagnosis of autism spectrum disorder (ASD) in children are beginning to use machine learning (ML) approaches applied to real-world clinical datasets, such as electronic health records (EHRs). However, sex-based disparities i...

Diagnostic Machine Learning Models of Infectious Mononucleosis in Children Based on Clinical Data: A Retrospective Multicenter Study.

Journal of medical virology
The clinical manifestations of infectious mononucleosis (IM) and acute respiratory tract infections (ARTI) exhibit significant similarities. We aim to develop cost-efficient models for IM in children utilizing the Shapley Additive explanation (SHAP) ...

Does restrictive anorexia nervosa impact brain aging? A machine learning approach to estimate age based on brain structure.

Computers in biology and medicine
Anorexia nervosa (AN), a severe eating disorder marked by extreme weight loss and malnutrition, leads to significant alterations in brain structure. This study used machine learning (ML) to estimate brain age from structural MRI scans and investigate...

Applying exposure-response analysis to enhance Mycophenolate Mofetil dosing precision in pediatric patients with immune-mediated renal diseases by machine learning models.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
BACKGROUND: Mycophenolate mofetil (MMF), a cornerstone immunosuppressant for lupus nephritis, is increasingly used off-label in pediatric immune-mediated renal diseases. The aims of this study were to develop and validate pharmacokinetic models for m...

Machine learning-based approaches for distinguishing viral and bacterial pneumonia in paediatrics: A scoping review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Pneumonia is the leading cause of hospitalisation and mortality among children under five, particularly in low-resource settings. Accurate differentiation between viral and bacterial pneumonia is essential for guiding approp...

The effect of social robot interventions on anxiety in children in clinical settings: a systematic review and meta-analysis.

Journal of affective disorders
AIMS: Children in clinical settings are prone to anxiety due to developmental limitations, which hinders treatment progress. This systematic review and meta-analysis aimed to evaluate the efficacy of social robot interventions compared to routine car...

Heart Rate and Body Temperature Relationship in Children Admitted to PICU: A Machine Learning Approach.

IEEE transactions on bio-medical engineering
UNLABELLED: Vital signs are crucial clinical measures, with body temperature (BT) and heart rate (HR) being particularly significant. While their association has been studied in adults and children, research in Pediatric Intensive Care Unit (PICU) se...