AIMC Topic: Young Adult

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Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes.

Scientific reports
Maternal mental health during pregnancy is a crucial area of research due to its profound impact on both maternal and child well-being. This paper proposes a comprehensive approach to predicting and monitoring psychological health risks in pregnant w...

Can circadian rhythms of heart rate variability identify major depressive disorder? - A study based on support vector machine analysis.

Asian journal of psychiatry
BACKGROUND: Major depressive disorder (MDD) is a prevalent and severe psychiatric condition for which objective diagnostic tools are lacking. Heart rate variability (HRV), an index of autonomic nervous system (ANS) function, has shown potential for d...

Enhancing AI literacy in undergraduate pre-medical education through student associations: an educational intervention.

BMC medical education
BACKGROUND: The integration of artificial intelligence (AI) into healthcare is rapidly advancing, with profound implications for medical practice. However, a gap exists in formal AI education for pre-medical students. This study evaluates the effecti...

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...

Specific media literacy tips improve AI-generated visual misinformation discernment.

Cognitive research: principles and implications
Images generated using artificial intelligence (AI) have become increasingly realistic, sparking discussions and fears about an impending "infodemic" where we can no longer trust what we see on the internet. In this preregistered study, we examine wh...

Influence of cognitive networks and task performance on fMRI-based state classification using DNN models.

Scientific reports
Deep neural networks (DNNs) excel at extracting insights from complex data across various fields, however, their application in cognitive neuroscience remains limited, largely due to the lack of approaches with interpretability. Here, we employ two d...

[An interpretable machine learning modeling method for the effect of manual acupuncture manipulations on subcutaneous muscle tissue].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion
OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques.

Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana.

BMC pregnancy and childbirth
BACKGROUND: Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robus...

Modeling student satisfaction in online learning using random forest.

Scientific reports
The rapid expansion of online education has intensified the need to investigate the multifactorial determinants of university students' satisfaction with digital learning platforms. While prior studies have often examined technical or pedagogical com...

The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms.

Scientific reports
Hematoporphyrin monomethyl ether-photodynamic therapy (HMME-PDT) is a safe and effective treatment for port-wine stain (PWS). Comprehensive methods for predicting HMME-PDT efficacy based on clinical factors are lacking. This study aims to develop and...