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Development and Validation of an Explainable Prediction Model for Postoperative Recurrence in Pediatric Chronic Rhinosinusitis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: This study aims to develop an interpretable machine learning (ML) predictive model to assess its efficacy in predicting postoperative recurrence in pediatric chronic rhinosinusitis (CRS).

Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children.

Critical care explorations
BACKGROUND: Continuous renal replacement therapy (CRRT) is the favored renal replacement therapy in critically ill patients. Predicting clinical outcomes for CRRT patients is difficult due to population heterogeneity, varying clinical practices, and ...

Prediction of talent selection in elite male youth soccer across 7 seasons: A machine-learning approach.

Journal of sports sciences
This study aimed to investigate the relative importance of parameters from several domains associated to both selecting or de-selecting players with regards to the next age group within a professional German youth soccer academy across a 7-year perio...

A Fusion Model of MRI Deep Transfer Learning and Radiomics for Discriminating between Pilocytic Astrocytoma and Adamantinomatous Craniopharyngioma.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a fusion model combining MRI deep transfer learning (DTL) and radiomics for discriminating between pilocytic astrocytoma (PA) and adamantinomatous craniopharyngioma (ACP) in the sella...

Case reports unlocked: Harnessing large language models to advance research on child maltreatment.

Child abuse & neglect
BACKGROUND: Research on child protective services (CPS) is impeded by a lack of high-quality structured data. Crucial information on cases is often documented in case files, but only in narrative form. Researchers have applied automated language proc...

Artificial intelligence in the care of children and adolescents with chronic diseases: a systematic review.

European journal of pediatrics
UNLABELLED: The integration of artificial intelligence (AI) and machine learning (ML) has shown potential for various applications in the medical field, particularly for diagnosing and managing chronic diseases among children and adolescents. This sy...

A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana.

PLoS neglected tropical diseases
BACKGROUND: Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000-138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths an...

Firearm Injury Risk Prediction Among Children Transported by 9-1-1 Emergency Medical Services: A Machine Learning Analysis.

Pediatric emergency care
OBJECTIVE: Among children transported by ambulance across the United States, we used machine learning models to develop a risk prediction tool for firearm injury using basic demographic information and home ZIP code matched to publicly available data...

Child-Centric Robot Dialogue Systems: Fine-Tuning Large Language Models for Better Utterance Understanding and Interaction.

Sensors (Basel, Switzerland)
Dialogue systems must understand children's utterance intentions by considering their unique linguistic characteristics, such as syntactic incompleteness, pronunciation inaccuracies, and creative expressions, to enable natural conversational engageme...

Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.

Renal failure
BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This stud...