AIMC Topic: Adolescent

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Using machine learning to identify features associated with different types of self-injurious behaviors in autistic youth.

Psychological medicine
BACKGROUND: Self-injurious behaviors (SIB) are common in autistic people. SIB is mainly studied as a broad category, rather than by specific SIB types. We aimed to determine associations of distinct SIB types with common psychiatric, emotional, medic...

Enhancing smart healthcare with female students' stress and anxiety detection using machine learning.

Psychology, health & medicine
Machine learning (ML) is widely used to predict and detect stress and anxiety. Early detection of stress or anxiety is crucial for clinical pathways to enhance the supportive environment in society, particularly among female students. This study aims...

Multivariate Classification of Adolescent Major Depressive Disorder Using Whole-brain Functional Connectivity.

Academic radiology
RATIONALE AND OBJECTIVES: Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional connectivity (FC) patterns within the brain. However, whether FC could be used as a potential biomar...

O blood usage trends in the pediatric population 2015-2019: A multi-institutional analysis.

Transfusion
BACKGROUND: In 2019, AABB released the bulletin "Recommendations on the Use of Group O Red Blood Cells" in which the recommendations about pediatric and neonatal blood transfusions were limited. Eight U.S. pediatric hospitals sought to determine tren...

Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach.

Head & face medicine
BACKGROUND: Facial aesthetics is one of major motivations for seeking orthodontic treatment. However, even for experienced professionals, the impact and extent of incisor and soft tissue changes remain largely empirical. With the application of inter...

Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China.

BMC pediatrics
OBJECTIVE: This study aims to characterize and analyze the expression of representative biomarkers like lymphocytes and immune subsets in children with thyroid disorders. It also intends to develop and evaluate a machine learning model to predict if ...

Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.

BMC cancer
BACKGROUND: Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert...

Footwork recognition and trajectory tracking in track and field based on image processing.

Scientific reports
In track and field sports, footwork can greatly affect the effect and performance of sports. Accurate footwork can effectively improve the performance of professional athletes, and for ordinary trainers, it can reduce the probability of training inju...

A large-scale multicenter study of reference intervals and clinical potential for homocysteine-folate cycle metabolites in Northern Chinese population.

Clinica chimica acta; international journal of clinical chemistry
OBJECTIVES: The study was conducted to establish the reference intervals of homocysteine-folate cycle metabolites based on the healthy population from multiple centers in northern China, and determine their clinical significance in the diagnosis of r...

A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.

BMC health services research
BACKGROUND: The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefo...