AIMC Topic: Adolescent

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Machine-learning random forest algorithms predict post-cycloplegic myopic corrections from noncycloplegic clinical data.

Optometry and vision science : official publication of the American Academy of Optometry
SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncycloplegic clinical data. A classification model predicted post-cycloplegic myopia and could be useful i...

How adaptive social robots influence cognitive, emotional, and self-regulated learning.

Scientific reports
As educational environments become more diverse, adaptive technologies like social robots hold promise for providing individual support to learners. This study investigated the role of adaptive teaching of a robot on students' learning outcomes, emot...

Validation of a machine learning model for indirect screening of suicidal ideation in the general population.

Scientific reports
Suicide is among the leading causes of death worldwide and a concerning public health problem, accounting for over 700,000 registered deaths worldwide. However, suicide deaths are preventable with timely and evidence-based interventions, which are of...

Machine Learning-Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: To address gaps in global understanding of cultural and social variations, this study used a high-performance machine learning (ML) model to predict adolescent substance use across three national datasets.

Machine learning using random forest to differentiate between blow and fall situations of head trauma.

International journal of legal medicine
Blunt head trauma is a common occurrence in forensic practice. Interpreting the origin of craniocerebral injuries can be a challenging process, particularly when it comes to distinguishing between falls or inflicted blows. The objective of this study...

Epilepsy surgery candidate identification with artificial intelligence: An implementation study.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: To (a) evaluate the effect of a machine learning algorithm in the identification of patients suitable for epilepsy surgery evaluation, and (b) examine the performance of a large language model (LLM) in the collation of key pieces of infor...

Population norms for the EQ-5D-5L for Hungary: comparison of online surveys and computer assisted personal interviews.

The European journal of health economics : HEPAC : health economics in prevention and care
BACKGROUND AND OBJECTIVES: The aims of this study were to provide population norms for EQ-5D-5L in Hungary and investigate the differences in EQ-5D-5L normative data by survey mode, i.e. online surveys and computer assisted personal interviews (CAPI)...

Neurobiologically interpretable causal connectome for predicting young adult depression: A graph neural network study.

Journal of affective disorders
BACKGROUND: There is a surprising lack of neuroimaging studies of depression that not only identify the whole brain causal connectivity features but also explore whether these features have neurobiological correlates.

Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review.

Computers in biology and medicine
BACKGROUND: The global mental health crisis has created barriers to youth mental healthcare, leaving many disorders unaddressed. Precision prevention, which identifies individual risks, offers the potential for tailored interventions. While natural l...

Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model.

BMC public health
BACKGROUND: In March 2022, a new outbreak of COVID-19 emerged in Quanzhou, leading to the implementation of strict lockdown management measures in colleges. While existing research has indicated that the pandemic has had a significant impact on sleep...