AIMC Topic: Adolescent

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Machine Learning Models for Predicting Pediatric Hospitalizations Due to Air Pollution and Humidity: A Retrospective Study.

Pediatric pulmonology
BACKGROUND: Exposure to air pollution and meteorological conditions, such as humidity, has been linked to adverse respiratory health outcomes in children. This study aims to develop predictive models for pediatric hospitalizations based on both envir...

Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) ...

Expectations of healthcare AI and the role of trust: understanding patient views on how AI will impact cost, access, and patient-provider relationships.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Although efforts to effectively govern AI continue to develop, relatively little work has been done to systematically measure and include patient perspectives or expectations of AI in governance. This analysis is designed to understand pa...

Deep Learning-based Aligned Strain from Cine Cardiac MRI for Detection of Fibrotic Myocardial Tissue in Patients with Duchenne Muscular Dystrophy.

Radiology. Artificial intelligence
Purpose To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these values to predict myocardial fibrosis in patients with Duchenne muscular dystrophy (DMD). Materials...

Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning.

JAMA psychiatry
IMPORTANCE: The diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence or early adulthood, which impedes initiation of targeted treatment.

Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection.

The Angle orthodontist
OBJECTIVES: To develop facial growth prediction models using artificial intelligence (AI) under various conditions, and to compare performance of these models with each other as well as with the partial least squares (PLS) growth prediction model.

Application of Machine Learning Techniques to the Prediction of Onset and Persistence of Binge Eating: A Prospective Study.

European eating disorders review : the journal of the Eating Disorders Association
OBJECTIVE: Machine learning (ML) techniques have shown promise for enhancing prediction of clinical outcomes; however, its application to predicting binge eating has been scarcely explored. We applied ML techniques to predict binge eating onset (vs. ...

Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumours Molecular Subtype Identification Using MRI-based 3D Probability Distributions of Tumour Location.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Pediatric low-grade gliomas (pLGG) are the most common brain tumour in children, and the molecular diagnosis of pLGG enables targeted treatment. We use MRI-based Convolutional Neural Networks (CNNs) for molecular subtype identification of pLGG and a...

Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
OBJECTIVE: The objective of this study was to analyze the incidence and overall survival (OS) of osteosarcoma (OSC) and Ewing's sarcoma (EWS) in a pediatric and adolescent population, employing machine learning (ML) and deep learning (DL) models to p...

Artificial Intelligence Promotes the Dunning Kruger Effect: Evaluating ChatGPT Answers to Frequently Asked Questions About Adolescent Idiopathic Scoliosis.

The Journal of the American Academy of Orthopaedic Surgeons
INTRODUCTION: Patients have long turned to the Internet for answers to common medical questions. As the ability to access information evolves beyond standard search engines, patients with adolescent idiopathic scoliosis (AIS) and their parents may us...