AIMC Topic: Adolescent

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Multi-task reinforcement learning and explainable AI-Driven platform for personalized planning and clinical decision support in orthodontic-orthognathic treatment.

Scientific reports
This study presents a novel clinical decision support platform for orthodontic-orthognathic treatment that integrates multi-task reinforcement learning with explainable artificial intelligence. The platform addresses the challenges of personalized tr...

Deep learning diagnosis plus kinematic severity assessments of neurodivergent disorders.

Scientific reports
Early diagnostic assessments of neurodivergent disorders (NDD), remains a major clinical challenge. We address this problem by pursuing the hypothesis that there is important cognitive information about NDD conditions contained in the way individuals...

School-Based Online Surveillance of Youth: Systematic Search and Content Analysis of Surveillance Company Websites.

Journal of medical Internet research
BACKGROUND: School-based online surveillance of students has been widely adopted by middle and high school administrators over the past decade. Little is known about the technology companies that provide these services or the benefits and harms of th...

Discrimination of Dengue Diseases in Children Using Surface-Enhanced Raman Spectroscopy Coupled with Machine Learning Approaches.

Analytical chemistry
This study introduces a novel approach to dengue diagnostics by leveraging surface-enhanced Raman spectroscopy (SERS) coupled to machine learning. This method addresses the critical need for rapid and accurate identification of dengue virus (DENV) in...

Whole‑exome evolutionary profiling of osteosarcoma uncovers metastasis‑related driver mutations and generates an independently validated predictive classifier.

Journal of translational medicine
BACKGROUND: Osteosarcoma is the most common primary malignant bone tumor, with high invasiveness and metastatic potential and a poor prognosis in patients with metastatic cancer. Despite the rapid advancements in genomics in recent years that provide...

Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data.

BMJ health & care informatics
OBJECTIVES: To identify and characterise distinct subgroups of patients with asthma with severe acute exacerbations (AEs) by using a multistep clustering methodology that combines supervised and unsupervised machine learning.

Employing machine learning for early detection of poly-victimization in rural children: a survey study in China's Chaoshan region.

BMC public health
BACKGROUND: Poly-victimization (PV), encompassing multiple forms of victimization including physical abuse, emotional maltreatment, neglect, and peer violence, poses a significant public health challenge among children, particularly in rural areas wi...

Assessing psychological resilience and its influencing factors in the MSM population by machine learning.

Scientific reports
This study assesses the influence of social support, self-esteem, depression, and education on psychological resilience among men who have sex with men (MSM) to inform policy-making. Data were collected from 1,070 MSM via an online survey in Zhejiang...

Developing self-regulated artificial intelligence learning (SRAIL) Student Attitudes Scale.

Acta psychologica
This research aims to develop a valid and reliable scale for measuring students' attitudes towards integrating artificial intelligence (AI) in self-regulated learning (SRL). In this study, 250 children from the Ankara province in Turkey participated....

Machine learning-based prediction of celiac antibody seropositivity by biochemical test parameters.

Scientific reports
The diagnostic delay in celiac disease (CD) is currently a burden for individual and society. Biochemical tests may be used in risk-identification of CD to reduce the diagnostic delay, and we aimed to explore prediction models for CD antibody seropos...