{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:22:45Z","timestamp":1784200965224,"version":"3.55.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"S2","license":[{"start":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T00:00:00Z","timestamp":1554076800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2019,4]]},"DOI":"10.1186\/s12911-019-0765-4","type":"journal-article","created":{"date-parts":[[2019,4,9]],"date-time":"2019-04-09T00:05:34Z","timestamp":1554768334000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records"],"prefix":"10.1186","volume":"19","author":[{"given":"Yafeng","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Fei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghong","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,4,4]]},"reference":[{"issue":"3","key":"765_CR1","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1161\/hc0302.102575","volume":"105","author":"G Assmann","year":"2002","unstructured":"Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular mnster (procam) study. Circulation. 2002; 105(3):310\u20135.","journal-title":"Circulation"},{"issue":"1","key":"765_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1053\/j.ajkd.2003.09.009","volume":"43","author":"KDOQ Initiative","year":"2004","unstructured":"Initiative KDOQ. K\/doqi clinical practice guidelines on hypertension and antihypertensive agents in chronic kidney disease. Am J Kidney Dis. 2004; 43(1):1\u2013290.","journal-title":"Am J Kidney Dis"},{"issue":"3","key":"765_CR3","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1161\/01.HYP.0000198544.09909.1a","volume":"47","author":"K Zandinejad","year":"2006","unstructured":"Zandinejad K, Luyckx VA, Brenner BM. Adult hypertension and kidney disease the role of fetal programming. Hypertension. 2006; 47(3):502.","journal-title":"Hypertension"},{"issue":"7659","key":"765_CR4","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1136\/bmj.39609.449676.25","volume":"336","author":"J Hippisleycox","year":"2008","unstructured":"Hippisleycox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, Brindle P. Predicting cardiovascular risk in england and wales: prospective derivation and validation of qrisk2. Bmj Br Med J. 2008; 336(7659):1475\u201382.","journal-title":"Bmj Br Med J"},{"issue":"7713","key":"765_CR5","first-page":"144","volume":"339","author":"GS Collins","year":"2009","unstructured":"Collins GS, Altman DG. An independent external validation and evaluation of qrisk cardiovascular risk prediction: a prospective open cohort study. Bmj. 2009; 339(7713):144\u20137.","journal-title":"Bmj"},{"issue":"1","key":"765_CR6","first-page":"1","volume":"14","author":"WW Chen","year":"2017","unstructured":"Chen WW, Gao RL, Liu LS, Zhu ML, Wang W, Wang YJ, Wu ZS, Li HJ, Gu DF, Yang YJ. China cardiovascular diseases report 2015: a summary. J Geriatr Cardiol Jgc. 2017; 14(1):1\u201310.","journal-title":"J Geriatr Cardiol Jgc"},{"issue":"10092","key":"765_CR7","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/S0140-6736(17)30576-7","volume":"390","author":"VA Luyckx","year":"2017","unstructured":"Luyckx VA, Perico N, Somaschini M, Manfellotto D, Valensise H, Cetin I, Simeoni U, Allegaert K, Vikse BE, Steegers EA. A developmental approach to the prevention of hypertension and kidney disease: a report from the low birth weight and nephron number working group. Lancet. 2017; 390(10092):424\u20138.","journal-title":"Lancet"},{"issue":"18","key":"765_CR8","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1161\/01.CIR.97.18.1837","volume":"97","author":"PWF Wilson","year":"1998","unstructured":"Wilson PWF, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998; 97(18):1837\u201347.","journal-title":"Circulation"},{"issue":"9","key":"765_CR9","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1016\/S0895-4356(03)00055-6","volume":"56","author":"LE Chambless","year":"2003","unstructured":"Chambless LE, Folsom AR, Sharrett AR, Sorlie P, Couper D, Szklo M, Nieto FJ. Coronary heart disease risk prediction in the atherosclerosis risk in communities (aric) study. J Clin Epidemiol. 2003; 56(9):880\u201390.","journal-title":"J Clin Epidemiol"},{"issue":"15","key":"765_CR10","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1093\/bioinformatics\/btv173","volume":"31","author":"S Agarwal","year":"2015","unstructured":"Agarwal S, Ghanty P, Pal NR. Identification of a small set of plasma signalling proteins using neural network for prediction of alzheimer\u2019s disease. Bioinformatics. 2015; 31(15):2505\u201313.","journal-title":"Bioinformatics"},{"key":"765_CR11","volume-title":"Proceedings of International Conference on Innovations in Bio-Inspired Computing and Applications.","author":"MA Jabbar","year":"2016","unstructured":"Jabbar MA, Deekshatulu BL, Chandra P. Prediction of heart disease using random forest and feature subset selection. In: Proceedings of International Conference on Innovations in Bio-Inspired Computing and Applications.Berlin: Springer: 2016. p. 187\u2013196."},{"issue":"3","key":"765_CR12","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s40595-016-0063-3","volume":"3","author":"DH Le","year":"2016","unstructured":"Le DH, Dang VT. Ontology-based disease similarity network for disease gene prediction. Vietnam J Comput Sci. 2016; 3(3):197\u2013205.","journal-title":"Vietnam J Comput Sci"},{"key":"765_CR13","volume-title":"IEEE International Conference on Bioinformatics and Biomedicine.","author":"X Meng","year":"2017","unstructured":"Meng X, Zou Q, Rodriguezpaton A, Zeng X. Iteratively collective prediction of disease-gene associations through the incomplete network. In: IEEE International Conference on Bioinformatics and Biomedicine.New York: IEEE: 2017. p. 1323\u201330."},{"key":"765_CR14","volume-title":"Proceedings of IEEE International Conference on Computational Advances in Bio and Medical Sciences.","author":"P Akram","year":"2017","unstructured":"Akram P, Li L. Prediction of missing common genes for disease pairs using network based module separation on incomplete human interactome. In: Proceedings of IEEE International Conference on Computational Advances in Bio and Medical Sciences.Berlin: Springer: 2017. p. 1."},{"key":"765_CR15","volume-title":"Proceedings of International Conference of the IEEE Engineering in Medicine & Biology Society.","author":"R Chen","year":"2017","unstructured":"Chen R, Yang Y, Miao F, Cai Y, Lin D, Zheng J, Li Y. 3-year risk prediction of coronary heart disease in hypertension patients: A preliminary study. In: Proceedings of International Conference of the IEEE Engineering in Medicine & Biology Society.New York: IEEE: 2017. p. 1182\u201385."},{"key":"765_CR16","volume-title":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.","author":"Y Ren","year":"2016","unstructured":"Ren Y, Zhang Y, Zhang M, Ji D. Context-sensitive twitter sentiment classification using neutal network. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.Palo Alto: Association for the Advancement of Artificial Intelligence: 2016. p. 215\u201321."},{"key":"765_CR17","doi-asserted-by":"crossref","unstructured":"Zeng D, Sun C, Lin L, Liu B. Lstm-crf for drug-named entity recognition. Entropy. 2017;19(6):1\u201312.","DOI":"10.3390\/e19060283"},{"key":"765_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2018.03.047","volume":"308","author":"Y Ren","year":"2018","unstructured":"Ren Y, Ji D, Ren H. Context-augmented convolutional neural networks for twitter sarcasm detection. Neurocomputing. 2018; 308:1\u20137.","journal-title":"Neurocomputing"},{"issue":"10","key":"765_CR19","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1371\/journal.pmed.0030374","volume":"3","author":"MN Weedon","year":"2006","unstructured":"Weedon MN, Mccarthy MI, Hitman G, Walker M, Groves CJ, Zeggini E, Rayner NW, Shields B, Owen KR, Hattersley AT. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. Plos Med. 2006; 3(10):374.","journal-title":"Plos Med"},{"issue":"6","key":"765_CR20","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.3945\/jn.111.157222","volume":"142","author":"SE Chiuve","year":"2012","unstructured":"Chiuve SE, Fung TT, Rimm EB, Hu FB, Mccullough ML, Wang M, Stampfer MJ, Willett WC. Alternative dietary indices both strongly predict risk of chronic disease. J Nutr. 2012; 142(6):1009.","journal-title":"J Nutr"},{"issue":"1","key":"765_CR21","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/s12942-015-0017-5","volume":"14","author":"A Flynt","year":"2015","unstructured":"Flynt A, Daepp MIG. Diet-related chronic disease in the northeastern united states: a model-based clustering approach. Int J Health Geogr. 2015; 14(1):25.","journal-title":"Int J Health Geogr"},{"issue":"1","key":"765_CR22","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s12881-017-0451-2","volume":"18","author":"GB Chen","year":"2017","unstructured":"Chen GB, Lee SH, Montgomery GW, Wray NR, Visscher PM, Gearry RB, Lawrance IC, Andrews JM, Bampton P, Mahy G. Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method. BMC Med Genet. 2017; 18(1):94.","journal-title":"BMC Med Genet"},{"issue":"16","key":"765_CR23","doi-asserted-by":"publisher","first-page":"1610","DOI":"10.1001\/jama.2010.461","volume":"303","author":"TS Polonsky","year":"2010","unstructured":"Polonsky TS, Mcclelland RL, Jorgensen NW, Bild DE, Burke GL, Guerci AD, Greenland P. Coronary artery calcium score and risk classification for coronary heart disease prediction. Jama. 2010; 303(16):1610.","journal-title":"Jama"},{"issue":"11 Suppl 5","key":"765_CR24","first-page":"45","volume":"11 suppl 5","author":"P. Cullen","year":"2001","unstructured":"Cullen P., Funke H.Implications of the human genome project for the identification of genetic risk of coronary heart disease and its prevention in children. Nutr Metab Cardiovasc Dis Nmcd. 2001; 11 suppl 5(11 Suppl 5):45\u201351.","journal-title":"Nutr Metab Cardiovasc Dis Nmcd"},{"issue":"1","key":"765_CR25","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1634\/stemcells.2006-0351","volume":"25","author":"P Guglielmelli","year":"2010","unstructured":"Guglielmelli P, Zini R, Bogani C, Salati S, Pancrazzi A, Bianchi E, Mannelli F, Ferrari S, Le BKM, Bosi A. Molecular profiling of cd34+ cells in idiopathic myelofibrosis identifies a set of disease-associated genes and reveals the clinical significance of wilms\u2019 tumor gene 1 (wt1). Stem Cells. 2010; 25(1):165\u201373.","journal-title":"Stem Cells"},{"issue":"10","key":"765_CR26","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1101\/gr.6665407","volume":"17","author":"NR Wray","year":"2007","unstructured":"Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 2007; 17(10):1520\u20138.","journal-title":"Genome Res"},{"key":"765_CR27","volume-title":"Proceedings of International Conference on Empirical Methods in Natural Language Processing.","author":"D Tang","year":"2015","unstructured":"Tang D, Qin B, Liu T. Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of International Conference on Empirical Methods in Natural Language Processing.Stroudsburg: Association for Computational Linguistics: 2015. p. 1422\u201332."},{"key":"765_CR28","volume-title":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.","author":"Y Ren","year":"2016","unstructured":"Ren Y, Zhang Y, Zhang M, Ji D. Improving twitter sentiment classification using topic-enriched multi-prototype word embeddings. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.Palo Alto: Association for the Advancement of Artificial Intelligence: 2016. p. 3038\u201344."},{"key":"765_CR29","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.ins.2017.01.015","volume":"385-386","author":"Y Ren","year":"2017","unstructured":"Ren Y, Ji D. Neural networks for deceptive opinion spam detection: an empirical study. Inf Sci. 2017; 385-386:213\u201324.","journal-title":"Inf Sci"},{"key":"765_CR30","unstructured":"Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. Comput Sci. 2014. arXiv preprint arXiv:1409.0473."},{"issue":"2","key":"765_CR31","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1504\/IJDMB.2016.076534","volume":"15","author":"Z Zhao","year":"2016","unstructured":"Zhao Z, Yang Z, Lin H, Wang J, Gao S. A protein-protein interaction extraction approach based on deep neural network. Int J Data Min Bioinforma. 2016; 15(2):145\u201364.","journal-title":"Int J Data Min Bioinforma"},{"issue":"2","key":"765_CR32","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/s12911-017-0468-7","volume":"17","author":"Z Liu","year":"2017","unstructured":"Liu Z, Yang M, Wang X, Chen Q, Tang B, Wang Z, Xu H. Entity recognition from clinical texts via recurrent neural network. BMC Med Inform Decis Mak. 2017; 17(2):67.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"765_CR33","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1186\/s12859-017-1868-5","volume":"18","author":"L Chen","year":"2017","unstructured":"Chen L, Chen B, Ren Y, Ji D. Long short-term memory rnn for biomedical named entity recognition. BMC Bioinformatics. 2017; 18(1):462\u201393.","journal-title":"BMC Bioinformatics"},{"key":"765_CR34","volume-title":"Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine","author":"Y Ren","year":"2018","unstructured":"Ren Y, Fei H, Peng Q. Detecting the scope of negation and speculation in biomedical texts by using recursive neural networks. In: Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine. New York: IEEE: 2018. p. 739\u201342."},{"issue":"C","key":"765_CR35","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.neucom.2017.02.066","volume":"243","author":"Z Fan","year":"2017","unstructured":"Fan Z, Bi D, He L, Ma S, Li C, Li C. Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder. Neurocomputing. 2017; 243(C):12\u201320.","journal-title":"Neurocomputing"},{"key":"765_CR36","volume-title":"Proceedings of the AAAI-98 Workshop on Learning for Text Categorization","author":"A Mccallum","year":"1998","unstructured":"Mccallum A, Nigam K. A comparison of event models for naive bayes text classification. In: Proceedings of the AAAI-98 Workshop on Learning for Text Categorization. Palo Alto: Association for the Advancement of Artificial Intelligence: 1998. p. 41\u201348."},{"key":"765_CR37","volume-title":"Proceedings of International Conference on Neural Information Processing Systems.","author":"L Mason","year":"1999","unstructured":"Mason L, Baxter J, Bartlett P, Frean M. Boosting algorithms as gradient descent. In: Proceedings of International Conference on Neural Information Processing Systems.Cambridge: MIT Press: 1999. p. 512\u20138."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-019-0765-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12911-019-0765-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-019-0765-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T14:24:15Z","timestamp":1663251855000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-019-0765-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4]]},"references-count":37,"journal-issue":{"issue":"S2","published-print":{"date-parts":[[2019,4]]}},"alternative-id":["765"],"URL":"https:\/\/doi.org\/10.1186\/s12911-019-0765-4","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4]]},"assertion":[{"value":"4 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"51"}}