AIMC Topic: Child, Preschool

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Prediction of moderate to severe bleeding risk in pediatric immune thrombocytopenia using machine learning.

European journal of pediatrics Apr 7, 2025
UNLABELLED: This study aimed to develop and validate a risk prediction model for moderate to severe bleeding in children with immune thrombocytopenia (ITP). Data from 286 ITP patients were prospectively collected and randomly split into training (80%...
Adolescent Child, Preschool Child Hemorrhage Male Risk Assessment Infant Severity of Illness Index Platelet Count Purpura, Thrombocytopenic, Idiopathic Machine Learning Humans Prospective Studies Female
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Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.

International journal of medical informatics Apr 4, 2025
OBJECTIVE: Determine the efficacy of commonly used approaches to handling missing and/or imbalanced Electronic Health Record (EHR) data on the performance of predictive models targeting risk of admission, intensive care unit (ICU) use, or prolonged l...
Intensive Care Units Electronic Health Records Female Hospitalization Humans Child Emergency Service, Hospital Fever Patient Admission Male Infant Length of Stay Child, Preschool
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Machine learning approach for dosage individualization of azithromycin in children with community-acquired pneumonia.

British journal of clinical pharmacology Apr 3, 2025
AIMS: The uncertainty about the efficacy and safety of currently used azithromycin dosing regimens in children warrants individualized therapy. The area under the plasma concentration-time curve over 24 h (AUC) of azithromycin correlates best with it...
Humans Community-Acquired Pneumonia Child, Preschool Child Female Dose-Response Relationship, Drug Machine Learning Male Pneumonia Algorithms Infant Anti-Bacterial Agents Area Under Curve Azithromycin Adolescent Precision Medicine Models, Biological Drug Dosage Calculations Community-Acquired Infections
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A machine learning model to predict intradialytic hypotension in pediatric continuous kidney replacement therapy.

Pediatric nephrology (Berlin, Germany) Apr 3, 2025
BACKGROUND: Intradialytic hypotension (IDH) is associated with mortality in adults undergoing intermittent hemodialysis, but this relationship is unclear in critically ill children receiving continuous kidney replacement therapy (CKRT). We aim to eva...
Continuous Renal Replacement Therapy Critical Illness Female Male Infant Humans Machine Learning Retrospective Studies Hospital Mortality Intensive Care Units, Pediatric ROC Curve Renal Dialysis Predictive Value of Tests Adolescent Hypotension Child, Preschool Child
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Contemporary digital marketing techniques used in unhealthy food campaigns targeting young people.

Appetite Apr 2, 2025
The digital marketing of unhealthy foods and non-alcoholic beverages has a detrimental impact on children's eating behaviours, leading to adverse diet-related health outcomes. To inform the development of evidence-based strategies to protect children...
Food Marketing Digital Technology Food Industry Food Preferences Feeding Behavior Child, Preschool Female Child Male Health Promotion Adolescent Beverages Humans
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Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.

Scientific reports Apr 2, 2025
Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest for an accurate and efficient method for early DIC prediction is of paramount importance. Candidate ...
Support Vector Machine Disseminated Intravascular Coagulation Adolescent Child, Preschool Critical Illness Child Female Logistic Models Infant, Newborn Male Humans Infant Machine Learning
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Trade-off of different deep learning-based auto-segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI.

Medical physics Apr 1, 2025
BACKGROUND: As auto-segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. One notable example is the use of adult-trained commercial software for the contouring of organs at ...
Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Automation Craniospinal Irradiation Deep Learning Humans Adolescent Tomography, X-Ray Computed Child, Preschool Child
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Automatic detection of developmental stages of molar teeth with deep learning.

BMC oral health Apr 1, 2025
BACKGROUND: The aim was to fully automate molar teeth developmental staging and to comprehensively analyze a wide range of deep learning models' performances for molar tooth germ detection on panoramic radiographs.
Female Young Adult Adult Male Child, Preschool Child Radiography, Panoramic Deep Learning Adolescent Humans Neural Networks, Computer Molar Tooth Germ
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A Tunable Forced Alignment System Based on Deep Learning: Applications to Child Speech.

Journal of speech, language, and hearing research : JSLHR Mar 31, 2025
PURPOSE: Phonetic forced alignment has a multitude of applications in automated analysis of speech, particularly in studying nonstandard speech such as children's speech. Manual alignment is tedious but serves as the gold standard for clinical-grade ...
Phonetics Speech Production Measurement Child Language Humans Female Speech Child, Preschool Child Male Deep Learning
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Using machine learning to identify features associated with different types of self-injurious behaviors in autistic youth.

Psychological medicine Mar 31, 2025
BACKGROUND: Self-injurious behaviors (SIB) are common in autistic people. SIB is mainly studied as a broad category, rather than by specific SIB types. We aimed to determine associations of distinct SIB types with common psychiatric, emotional, medic...
Adolescent Young Adult Self-Injurious Behavior Child, Preschool Humans Adult Child Machine Learning Autistic Disorder Male Autism Spectrum Disorder Surveys and Questionnaires Female
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