AIMC Topic: Adolescent

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Beyond health: A machine learning analysis of structural barriers to school attainment in Somalia.

PloS one
In fragile states like Somalia, the link between poor health and educational exclusion is critical yet poorly understood. This study uses a novel machine learning approach to identify and rank the most significant barriers to school attendance. We an...

A neuro-fuzzy model for evaluating and predicting computational thinking skills of students.

Scientific reports
Computational thinking skill is an important skill individuals should acquire to meet the requirements of the digital age. The aim of the study is to predict the computational thinking skills of middle school students through ANFIS approach, which is...

A novel machine-learning-based model for prediction of open gingival embrasures between mandibular central incisors after clear aligners treatment: a retrospective cohort study.

Progress in orthodontics
OBJECTIVE: To develop a machine-learning-based model and construct a nomogram that integrates ClinCheck features and clinical risk factors for accurately predicting open gingival embrasures (OGE) between mandibular central incisors after clear aligne...

Brain dynamics and depressive symptoms in young adults: Evidence from EEG.

Journal of affective disorders
BACKGROUND: Depression is a major public health concern, with a rising prevalence among adolescents and young adults. However, the neural mechanisms underlying depressive symptoms remain poorly understood. This study aimed to identify patterns of alt...

Prevalence, associated factors, and machine learning-based prediction of depression, anxiety, and stress among university students: a cross-sectional study from Bangladesh.

Journal of health, population, and nutrition
BACKGROUND: Mental health challenges are a growing global public health concern, with university students at elevated risk due to academic and social pressures. Although several studies have exmanined mental health among Bangladeshi students, few hav...

Validity and reliability analysis of the Turkish life satisfaction scale developed through artificial intelligence.

BMC psychology
This study evaluates the validity and reliability of a Turkish Life Satisfaction Scale developed using artificial intelligence (ChatGPT) to explore AI's potential in creating psychometric tools. The scale was tested on three independent samples of Tu...

Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia.

Orphanet journal of rare diseases
BACKGROUND: Significant diagnostic delays are common in primary ciliary dyskinesia (PCD), a rare disease that is significantly underdiagnosed. Scalable screening methods could improve early identification and health outcomes.

Prevalence and factors associated with HIV drug resistance among adult persons living with HIV/AIDS in nine countries of Sub-Saharan Africa using population-based HIV impact assessments: 2015-2019.

BMC public health
INTRODUCTION: HIV drug resistance (HIVDR) remains a significant challenge in sub-Saharan Africa (SSA) due to limited effective Treatment and healthcare resources vary. Using the first widely available HIVDR surveillance data in SSA, we calculated the...

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting.

BMC primary care
BACKGROUND: Establishing risk factors associated with severity and prognosis in the early stages of the disease is important to identify patients who need specialized care. Creating new clinical tools to improve health decisions and outcomes in the p...

Identifying key influencers of patient satisfaction using an explainable machine learning approach.

Scientific reports
Patient satisfaction is a crucial measure of healthcare quality, influencing both health outcomes and care experiences. This study aims to identify the factors influencing patient satisfaction in healthcare facilities using machine learning algorithm...