AIMC Topic: Young Adult

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Interpretable machine learning for cardiovascular risk prediction: Insights from NHANES dietary and health data.

PloS one
BACKGROUND: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination...

Cracking the code: a head-to-head comparison of expert clinicians and artificial intelligence in diagnosing rare diseases.

Orphanet journal of rare diseases
BACKGROUND: Patients with rare diseases often face prolonged diagnostic journeys due to the low prevalence and diverse clinical presentations of these conditions. In Germany, specialized centers for rare diseases, established at university hospitals,...

Artificial intelligence-based chatbots improve the efficiency of course orientation among medical students: a cross-sectional study.

BMC medical education
BACKGROUND: Large language models (LLMs) like ChatGPT offer new ways to improve academic and administrative workflows in medical education, particularly for students studying in a language that is not their native tongue. We set out to examine whethe...

Learning to Program "Recycles" Preexisting Frontoparietal Population Codes of Logical Algorithms.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Computer programming is a cornerstone of modern society, yet little is known about how the human brain enables this recently invented cultural skill. According to the neural recycling hypothesis, cultural skills (e.g., reading, math) repurpose preexi...

Distinct Portions of Superior Temporal Sulcus Combine Auditory Representations with Different Visual Streams.

The Journal of neuroscience : the official journal of the Society for Neuroscience
In humans, the superior temporal sulcus (STS) combines auditory and visual information. However, the extent to which it relies on visual information from the ventral or dorsal stream remains uncertain. To address this, we analyzed open-source functio...

Demographic influences on trust in artificial intelligence across cognitive domains: A statistical perspective.

PloS one
As artificial intelligence (AI) systems become increasingly integrated into decision-making across various sectors, understanding public trust in these systems is more crucial than ever. This study presents a quantitative analysis of survey data from...

Robot or human? Manoeuvring switching intention after robot service failure.

PloS one
This study attempts to scrutinise tourists' switching intentions towards human service after a robot service failure, with the zone of tolerance and trust on stance in technology as moderators. The study adopts the unified theory of acceptance and us...

Explainable artificial intelligence for predictive modeling of student stress in higher education.

Scientific reports
Student stress in higher education remains a pervasive problem, yet many institutions lack affordable, scalable, and interpretable tools for its detection and management. Existing methods frequently depend on costly physiological sensors and opaque m...

Machine learning to predict the role of CHWs in shifting birth preferences away from homebirth in India.

Scientific reports
This study utilized well-known supervised machine learning algorithms to NFHS‑5 data of West Bengal, India, to predict the place of birth (home vs facility) by integrating CHW (community health worker) contact factors and women participant's percepti...

An interpretable machine learning model predicts the interactive and cumulative risks of different environmental chemical exposures on depression.

Translational psychiatry
Humans are exposed to a multitude of environmental chemical mixtures (ECMs) in daily life that may influence depression risk. While prior studies have shown individual ECM exposures to depression, the cumulative and interactive effects of multiple co...