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Prediction of adolescent weight status by machine learning: a population-based study.

• Authors : Liu, Hengyan¹ (AUTHOR); Wu, Yik-Chung² (AUTHOR); Chau, Pui Hing¹ (AUTHOR)

Subjects: *MACHINE learning; *DIETARY patterns; *TEENAGERSHONG Kong (China)

• Source: BMC Public Health. 5/20/2024, Vol. 24 Issue 1, p1-13. 13p.

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An ecological examination of early adolescent e-cigarette use: A machine learning approach to understanding a health epidemic.

• Authors : Vázquez, Alejandro L.¹ (AUTHOR) ; Navarro Flores, Cynthia M.¹ (AUTHOR); Garcia, Byron H.² (AUTHOR)

Subjects: *AT-risk youth; *RISK perception; *ELECTRONIC cigarettes

• Source: PLoS ONE. 2/14/2024, Vol. 19 Issue 2, p1-13. 13p.

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Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression.

• Authors : Gunlicks-Stoessel, Meredith¹ (AUTHOR) ; Liu, Yangchenchen² (AUTHOR); Parkhill, Catherine¹ (AUTHOR)

• Source: BMC Medical Informatics & Decision Making. 1/2/2024, Vol. 24 Issue 1, p1-11. 11p.

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Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques.

• Authors : Marqués-Sánchez, Pilar¹ (AUTHOR); Martínez-Fernández, María Cristina¹ (AUTHOR); Benítez-Andrades, José Alberto² (AUTHOR)

Subjects: *SOCIAL network analysis; *MACHINE learning; *TEENAGERS

• Source: PLoS ONE. 8/15/2023, Vol. 18 Issue 8, p1-14. 14p.

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Sports Injury Identification Method Based on Machine Learning Model.

• Authors : Liu, Zexu¹ (AUTHOR); Zhang, Jun¹ (AUTHOR); Wu, Di¹ (AUTHOR)

• Source: Computational Intelligence & Neuroscience. 8/8/2022, p1-13. 13p.

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Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach.

• Authors : Weller, Orion^1,2 (AUTHOR) ; Sagers, Luke^3,4 (AUTHOR); Hanson, Carl⁵ (AUTHOR)

Subjects: *SUICIDAL behavior; *TEENAGERS; *SUICIDAL ideationUTAH

• Source: PLoS ONE. 11/3/2021, Vol. 16 Issue 11, p1-17. 17p.

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Predicting chronological age of 14 or 18 in adolescents: integrating dental assessments with machine learning.

• Authors : Shen, Shihui; Guo, Yibo; Han, Jiaxuan

Subjects: AGE; MACHINE learning; MOLARS

• Source: BMC Pediatrics; 4/10/2024, Vol. 24 Issue 1, p1-9, 9p

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Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology.

• Authors : Sideridou, Maria; Kouidi, Evangelia; Hatzitaki, Vassilia

Subjects: SIGNAL processing; RANGE of motion of joints; COMPUTER vision

• Source: Sensors (14248220); Apr2024, Vol. 24 Issue 7, p2037, 18p

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Identifying intentional injuries among children and adolescents based on Machine Learning.

• Authors : Yin, Xiling¹ (AUTHOR); Ma, Dan¹ (AUTHOR); Zhu, Kejing¹ (AUTHOR)

Subjects: *MACHINE learning; *TEENAGERS; *WOUNDS & injuries

• Source: PLoS ONE. 1/20/2021, Vol. 16 Issue 1, p1-15. 15p.

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National student survey metrics: where is the room for improvement?

• Authors : Langan, A. Mark¹ ; Harris, W. Edwin¹

• Source: Higher Education (00181560). Dec2019, Vol. 78 Issue 6, p1075-1089. 15p. 1 Chart, 4 Graphs.

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