AIMC Topic: Infant
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Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.
European respiratory review : an official journal of the European Respiratory Society
Jan 8, 2025
INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the me...
Fine-Grained Fidgety Movement Classification Using Active Learning.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Typically developing infants, between the corrected age of 9-20 weeks, produce fidgety movements. These movements can be identified with the General Movement Assessment, but their identification requires trained professionals to conduct the assessmen...
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...
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