{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T13:47:02Z","timestamp":1758808022689,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T00:00:00Z","timestamp":1626134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T00:00:00Z","timestamp":1626134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61702027"],"award-info":[{"award-number":["61702027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hunan Provincial Science and Technology Innovation Program","award":["2019SK2051"],"award-info":[{"award-number":["2019SK2051"]}]},{"name":"Aier Eye Hospital Group","award":["AR1903D3"],"award-info":[{"award-number":["AR1903D3"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s11063-021-10576-w","type":"journal-article","created":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T10:24:13Z","timestamp":1626171853000},"page":"247-257","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Approach of Combining Convolution Neural Network and Graph Convolution Network to Predict the Progression of Myopia"],"prefix":"10.1007","volume":"55","author":[{"given":"Lei","family":"Li","sequence":"first","affiliation":[]},{"given":"Haogang","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Longbo","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Weizhong","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Zhikuan","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,13]]},"reference":[{"issue":"9827","key":"10576_CR1","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1016\/S0140-6736(12)60272-4","volume":"379","author":"IG Morgan","year":"2012","unstructured":"Morgan IG, Onho-Matsui K, Saw SM (2012) Myopia. Lancet 379(9827):1739\u20131748","journal-title":"Lancet"},{"issue":"2","key":"10576_CR2","first-page":"332","volume":"43","author":"SM Saw","year":"2002","unstructured":"Saw SM, Chua WH, Hong CY et al (2002) Nearwork in early-onset myopia. Invest Ophthalmol Vis Sci 43(2):332\u2013339","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"7","key":"10576_CR3","doi-asserted-by":"publisher","first-page":"2903","DOI":"10.1167\/iovs.07-0804","volume":"49","author":"JM Ip","year":"2008","unstructured":"Ip JM, Saw SM, Rose KA et al (2008) Role of near work in myopia: findings in a sample of Australian school children. Invest Ophthalmol Vis Sci 49(7):2903\u20132910","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"12","key":"10576_CR4","first-page":"3633","volume":"43","author":"DO Mutti","year":"2002","unstructured":"Mutti DO, Mitchell GL, Moeschberger ML et al (2002) Parental myopia, near work, school achievement, and children\u2019s refractive error. Invest Ophthalmol Vis Sci 43(12):3633\u20133640","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"8","key":"10576_CR5","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1016\/j.ophtha.2007.12.019","volume":"115","author":"KA Rose","year":"2008","unstructured":"Rose KA, Morgan IG, Ip J et al (2008) Outdoor activity reduces the prevalence of myopia in children. Ophthalmology 115(8):1279\u20131285","journal-title":"Ophthalmology"},{"issue":"4","key":"10576_CR6","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1001\/archopht.126.4.527","volume":"126","author":"KA Rose","year":"2008","unstructured":"Rose KA, Morgan IG, Smith W et al (2008) Myopia, lifestyle, and schooling in students of Chinese ethnicity in Singapore and Sydney. Arch Ophthalmol 126(4):527\u2013530","journal-title":"Arch Ophthalmol"},{"issue":"1","key":"10576_CR7","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.exer.2006.09.018","volume":"84","author":"CS McCarthy","year":"2007","unstructured":"McCarthy CS, Megaw P, Devadas M et al (2007) Dopaminergic agents affect the ability of brief periods of normal vision to prevent form-deprivation myopia. Exp Eye Res 84(1):100\u2013107","journal-title":"Exp Eye Res"},{"key":"10576_CR8","doi-asserted-by":"crossref","unstructured":"Ester M, Kriegel HP, Sander J (1997) Spatial data mining: a database approach. In: International symposium on spatial databases. Springer, Berlin, Heidelberg, pp. 47\u201366","DOI":"10.1007\/3-540-63238-7_24"},{"key":"10576_CR9","doi-asserted-by":"crossref","unstructured":"Yang T, Gong YS (2008) Spatial data mining features between general data mining. In: 2008 International workshop on education technology and training & 2008 international workshop on geoscience and remote sensing. IEEE 2:541\u2013544","DOI":"10.1109\/ETTandGRS.2008.167"},{"issue":"7\u20138","key":"10576_CR10","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1016\/0893-6080(95)00061-5","volume":"8","author":"SCB Lo","year":"1995","unstructured":"Lo SCB, Chan HP, Lin JS et al (1995) Artificial convolution neural network for medical image pattern recognition. Neural Netw 8(7\u20138):1201\u20131214","journal-title":"Neural Netw"},{"key":"10576_CR11","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.ecoinf.2018.10.002","volume":"48","author":"BB Traore","year":"2018","unstructured":"Traore BB, Kamsu-Foguem B, Tangara F (2018) Deep convolution neural network for image recognition. Eco Inform 48:257\u2013268","journal-title":"Eco Inform"},{"key":"10576_CR12","unstructured":"Liu T, Fang S, Zhao Y, et al. (2015) Implementation of training convolutional neural networks. arXiv preprint arXiv:1506.01195"},{"key":"10576_CR13","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cviu.2017.05.007","volume":"161","author":"D Mishkin","year":"2017","unstructured":"Mishkin D, Sergievskiy N, Matas J (2017) Systematic evaluation of convolution neural network advances on the imagenet. Comput Vis Image Underst 161:11\u201319","journal-title":"Comput Vis Image Underst"},{"issue":"5","key":"10576_CR14","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2018","unstructured":"Cui P, Wang X, Pei J et al (2018) A survey on network embedding. IEEE Trans Knowl Data Eng 31(5):833\u2013852","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10576_CR15","doi-asserted-by":"crossref","unstructured":"Bacciu D, Errica F, Micheli A et al (2020) A gentle introduction to deep learning for graphs. Neural Netw 129:203\u2013221","DOI":"10.1016\/j.neunet.2020.06.006"},{"key":"10576_CR16","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Chen F et al (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"10576_CR17","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"10576_CR18","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, et al. (2018) Modeling relational data with graph convolutional networks. Eur Semant Web Conf. Springer, Cham, pp. 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"10576_CR19","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu XM (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence. 32(1)","DOI":"10.1609\/aaai.v32i1.11604"},{"issue":"11","key":"10576_CR20","first-page":"1542","volume":"104","author":"L Wen","year":"2020","unstructured":"Wen L, Cao Y, Cheng Q et al (2020) Objectively measured near work, outdoor exposure and myopia in children. Br J Ophthalmol 104(11):1542\u20131547","journal-title":"Br J Ophthalmol"},{"issue":"6","key":"10576_CR21","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1167\/tvst.8.6.15","volume":"8","author":"L Wen","year":"2019","unstructured":"Wen L, Cheng Q, Lan W et al (2019) An objective comparison of light intensity and near-visual tasks between rural and urban school children in China by a wearable device Clouclip. Transl Vis Sci Technol 8(6):15\u201315","journal-title":"Transl Vis Sci Technol"},{"issue":"9","key":"10576_CR22","first-page":"6454","volume":"60","author":"L Li","year":"2019","unstructured":"Li L, Zhu H, Wen L et al (2019) Association of myopia progression with visual behavior. Invest Ophthalmol Vis Sci 60(9):6454\u20136454","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"9","key":"10576_CR23","doi-asserted-by":"publisher","first-page":"3394","DOI":"10.1167\/iovs.17-22232","volume":"59","author":"L Li","year":"2018","unstructured":"Li L, Zhu H, Wen L et al (2018) An objective environmental risk factor index related to the development of myopia. Invest Ophthalmol Vis Sci 59(9):3394\u20133394","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"13","key":"10576_CR24","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1167\/tvst.9.13.17","volume":"9","author":"L Li","year":"2020","unstructured":"Li L, Wen L, Lan W et al (2020) A novel approach to quantify environmental risk factors of myopia: combination of wearable devices and big data science. Transl Vis Sci Technol 9(13):17\u201317","journal-title":"Transl Vis Sci Technol"},{"key":"10576_CR25","doi-asserted-by":"crossref","unstructured":"Li J, Liu X, Xiao J, et al. (2019) Dynamic spatio-temporal feature learning via graph convolution in 3D convolutional networks. In: 2019 International conference on data mining workshops (ICDMW). IEEE Computer Society, pp. 646\u2013652.","DOI":"10.1109\/ICDMW.2019.00098"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10576-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10576-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10576-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T14:18:57Z","timestamp":1678112337000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10576-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,13]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["10576"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10576-w","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2021,7,13]]},"assertion":[{"value":"29 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}