AIMC Topic: Child, Preschool
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Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan.
PloS one
Dec 31, 2024
Kawasaki Disease (KD) is a rare febrile illness affecting infants and young children, potentially leading to coronary artery complications and, in severe cases, mortality if untreated. However, KD is frequently misdiagnosed as a common fever in clini...
Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.
Scientific reports
Dec 28, 2024
The purpose of this study was to evaluate whether the optimal operating points of adult-oriented artificial intelligence (AI) software differ for pediatric chest radiographs and to assess its diagnostic performance. Chest radiographs from patients un...
Prediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning.
International journal of molecular sciences
Dec 27, 2024
Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives of children, making it essential to find effective prognostic markers to accurately predict their clinical outcomes. The development of high-throughp...
Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data.
Nutrition (Burbank, Los Angeles County, Calif.)
Dec 24, 2024
OBJECTIVES: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the ...
Predicting the infecting dengue serotype from antibody titre data using machine learning.
PLoS computational biology
Dec 23, 2024
The development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant challenge for vaccine development has been defining and measuring serotype-specific outcomes and correla...
Do machine learning methods solve the main pitfall of linear regression in dental age estimation?
Forensic science international
Dec 20, 2024
INTRODUCTION: Age estimation is crucial in forensic and anthropological fields. Teeth, are valued for their resilience to environmental factors and their preservation over time, making them essential for age estimation when other skeletal remains det...
Ocular biometric parameters in Chinese preschool children and physiological axial length growth prediction using machine learning algorithms: a retrospective cross-sectional study.
BMJ open
Dec 20, 2024
OBJECTIVES: To examine the ocular biometric parameters and predict the annual growth rate of the physiological axial length (AL) in Chinese preschool children aged 4-6 years old.
Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: a systematic review.
BMC cardiovascular disorders
Dec 20, 2024
INTRODUCTION: Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools...
Humans
Artificial Intelligence
Decision Support Techniques
Adolescent
Time Factors
Child, Preschool
Child
Treatment Outcome
Predictive Value of Tests
Cardiac Surgical Procedures
Adult
Heart Defects, Congenital
Female
Male
Infant, Newborn
Risk Assessment
Risk Factors
Postoperative Complications
Young Adult
Infant
Accuracy deficits during robotic time-constrained reaching are related to altered prefrontal cortex activity in children with cerebral palsy.
Journal of neuroengineering and rehabilitation
Dec 19, 2024
BACKGROUND: The prefrontal cortex (PFC) is an important node for action planning in the frontoparietal reaching network but its role in reaching in children with cerebral palsy (CP) is unexplored. This case-control study combines a robotic task with ...
Clinical characteristics and prediction model of re-positive nucleic acid tests among Omicron infections by machine learning: a real-world study of 35,488 cases.
BMC infectious diseases
Dec 18, 2024
BACKGROUND: During the Omicron BA.2 variant outbreak in Shanghai, China, from April to May 2022, PCR nucleic acid test re-positivity (TR) occurred frequently, yet the risk factors and predictive models for TR remain unclear. This study aims to identi...
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