Artificial Intelligence for Personalized Preventive Adolescent Healthcare.

J Adolesc Health

Department of Computer Science, College of Engineering, North Carolina State University, Raleigh, North Carolina.

Published: August 2020

Recent advances in artificial intelligence (AI) are creating new opportunities for personalizing technology-based health interventions to adolescents. This article provides a computer science perspective on how emerging AI technologies-intelligent learning environments, interactive narrative generation, user modeling, and adaptive coaching-can be utilized to model adolescent learning and engagement and deliver personalized support in adaptive health technologies. Many of these technologies have emerged from human-centered applications of AI in education, training, and entertainment. However, their application to improving healthcare, to date, has been comparatively limited. We illustrate the opportunities provided by AI-driven adaptive technologies for adolescent preventive healthcare by describing a vision of how future adolescent preventive health interventions might be delivered both inside and outside of the clinic. Key challenges posed by AI-driven health technologies are also presented, including issues of privacy, ethics, encoded bias, and integration into clinical workflows and adolescent lives. Examples of empirical findings about the effectiveness of AI technologies for user modeling and adaptive coaching are presented, which underscore their promise for application toward adolescent health. The article concludes with a brief discussion of future research directions for the field, which is well positioned to leverage AI to improve adolescent health and well-being.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jadohealth.2020.02.021DOI Listing

Publication Analysis

Top Keywords

artificial intelligence
8
health interventions
8
user modeling
8
modeling adaptive
8
health technologies
8
adolescent preventive
8
adolescent health
8
adolescent
7
health
6
technologies
5

Similar Publications

Background: Granulosa cell proliferation and survival are essential for normal ovarian function and follicular development. Long non-coding RNAs (lncRNAs) have emerged as important regulators of cell proliferation and differentiation. Nuclear paraspeckle assembly transcript 1 (NEAT1) has been implicated in various cellular processes, but its role in granulosa cell function remains unclear.

View Article and Find Full Text PDF

Cranioventral pulmonary consolidation (CVPC) is a common lesion observed in the lungs of slaughtered pigs, often associated with Mycoplasma (M.) hyopneumoniae infection. There is a need to implement simple, fast, and valid CVPC scoring methods.

View Article and Find Full Text PDF

Objectives: This data note presents a comprehensive geodatabase of cardiovascular disease (CVD) hospitalizations in Mashhad, Iran, alongside key environmental factors such as air pollutants, built environment indicators, green spaces, and urban density. Using a spatiotemporal dataset of over 52,000 hospitalized CVD patients collected over five years, the study supports approaches like advanced spatiotemporal modeling, artificial intelligence, and machine learning to predict high-risk CVD areas and guide public health interventions.

Data Description: This dataset includes detailed epidemiologic and geospatial information on CVD hospitalizations in Mashhad, Iran, from January 1, 2016, to December 31, 2020.

View Article and Find Full Text PDF

Background: Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers.

View Article and Find Full Text PDF

Background: Artificial intelligence has gradually been used into various fields of medical education at present. Under the background of moxibustion robot teaching assistance, the study aims to explore the relationship and the internal mechanism between learning engagement and evaluation in three stages, preparation before class, participation in class, and consolidation after class.

Methods: Based on the data investigated in 250 youths in university via multistage cluster sampling following the self-administered questionnaire, structural equation model was built to discussing factors of study process about moxibustion robots.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!