AIMC Topic: Infant
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Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study.
Scientific reports
Jan 6, 2025
Kawasaki disease (KD) is a leading cause of acquired heart disease in children, often resulting in coronary artery complications such as dilation, aneurysms, and stenosis. While intravenous immunoglobulin (IVIG) is effective in reducing immunologic i...
Assessing fecal contamination from human and environmental sources using as an indicator in rural eastern Ethiopian households-a cross-sectional study from the EXCAM project.
Frontiers in public health
Jan 6, 2025
INTRODUCTION: Enteric pathogens are a leading causes of diarrheal deaths in low-and middle-income countries. The Exposure Assessment of Infections in Rural Ethiopia (EXCAM) project, aims to identify potential sources of bacteria in the genus and, m...
Developing a simplified measure to predict the risk of autism spectrum disorders: Abbreviating the M-CHAT-R using a machine learning approach in China.
Psychiatry research
Jan 3, 2025
BACKGROUND: Early screening for autism spectrum disorder (ASD) is crucial, yet current assessment tools in Chinese primary child care are limited in efficacy.
Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.
Nature communications
Jan 2, 2025
Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness...
Application of machine learning in predicting postoperative arrhythmia following transcatheter closure of perimembranous ventricular septal defects.
Kardiologia polska
Jan 2, 2025
BACKGROUND: Arrhythmia is a frequent complication following transcatheter device closure of perimembranous ventricular septal defects (pmVSD). However, there is currently a lack of a convenient tool for predicting postoperative arrhythmia.
Forecasting Pediatric Trauma Volumes: Insights From a Retrospective Study Using Machine Learning.
The Journal of surgical research
Dec 31, 2024
INTRODUCTION: Rising pediatric firearm-related fatalities in the United States strain Trauma Centers. Predicting trauma volume could improve resource management and preparedness, particularly if daily forecasts are achievable. The aim of the study is...
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 ...
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