{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T08:08:13Z","timestamp":1767168493268,"version":"build-2238731810"},"reference-count":28,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2021,8,19]]},"abstract":"<jats:p>Young people\u2019s physical and mental health is the foundation of society\u2019s overall development and the key to improving people\u2019s health quality. Middle school students\u2019 physical examinations and monitoring work are a surefire way to ensure their healthy development. Poor vision, dental caries, overweight and obesity, and high blood pressure are the most common adverse health outcomes of students caused by adolescent health risk behavior factors. Researchers have been concerned about the retinal fundus vascular system, which is the only internal vascular system that can be observed in a noninvasive state of the human body. Fundus images contain a wealth of disease-related information. Fundus images have been widely used in the field of medical auxiliary diagnosis because many important systemic diseases of the human body cause specific reactions in the fundus. Aiming to solve the problem of inseparable tiny blood vessels, this paper proposes a model of retinal vessel segmentation based on attention mechanisms. In light of the retinal arteriovenous division of discontinuous challenges, the topological structure of the constraint system along with overcoming the network and topology restrictions is monitored. Finally, simulation experiments were conducted on two publicly available datasets. The findings show that the proposed method is reliable, effective, and accurate in predicting physical health risk factors in adolescent students.<\/jats:p>","DOI":"10.1155\/2021\/9049266","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T18:50:06Z","timestamp":1629399006000},"page":"1-8","source":"Crossref","is-referenced-by-count":5,"title":["Prediction Algorithm of Young Students\u2019 Physical Health Risk Factors Based on Deep Learning"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0161-8331","authenticated-orcid":true,"given":"Xianping","family":"Yin","sequence":"first","affiliation":[{"name":"Hangzhou Wan Xiang Polytechnic, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1186\/s12955-016-0415-9"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1093\/eurpub\/ckl051"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16203877"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jadohealth.2015.05.011"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-020-05845-8"},{"issue":"13","key":"6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/016146811812001304","article-title":"Examining the every student succeeds act\u2019s impact on African American students\u2019 mental health access","volume":"120","author":"L. 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