{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:52:58Z","timestamp":1740135178191,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"S15","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"vor","delay-in-days":23,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Laboratory Innovative Research Program of Shanghai Jiao Tong University","award":["AO612009\/065"],"award-info":[{"award-number":["AO612009\/065"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Cognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline\/impairment (MCI) can further develop into Dementia\/Alzheimer\u2019s disease. While treatment of Dementia\/Alzheimer\u2019s disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, based on the data collected by Centers for Disease Control and Prevention (CDC) through the nationwide telephone survey, we apply a data-driven approach to re-exam the previously founded risk factors and discover new risk factors. We found that depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery. Besides that, the first time, we point out that other factors such as arthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is less exploited previously. We also incorporate some machine learning and deep learning algorithms to weigh the importance of various factors contributed to MCI and predicted cognitive declined.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>By incorporating the data-driven approach, we can determine that risk factors significantly correlated with diseases. These correlations could also be expanded to another medical diagnosis besides MCI.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-019-3057-1","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T09:02:35Z","timestamp":1577178155000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Mild cognitive impairment understanding: an empirical study by data-driven approach"],"prefix":"10.1186","volume":"20","author":[{"given":"Liyuan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingchen","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7472-0842","authenticated-orcid":false,"given":"Meng","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"3057_CR1","doi-asserted-by":"publisher","first-page":"4331","DOI":"10.1136\/bmj.d4331","volume":"343","author":"RD Sanders","year":"2011","unstructured":"Sanders RD, Pandharipande PP, Davidson AJ, Ma D, Maze M. Anticipating and managing postoperative delirium and cognitive decline in adults. Bmj. 2011; 343:4331.","journal-title":"Bmj"},{"issue":"44","key":"3057_CR2","first-page":"743","volume":"108","author":"T Etgen","year":"2011","unstructured":"Etgen T, Sander D, Bickel H, F\u00f6rstl H. Mild cognitive impairment and dementia: the importance of modifiable risk factors. Deutsches \u00c4rzteblatt Int. 2011; 108(44):743.","journal-title":"Deutsches \u00c4rzteblatt Int"},{"issue":"23","key":"3057_CR3","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.1056\/NEJMcp0910237","volume":"364","author":"RC Petersen","year":"2011","unstructured":"Petersen RC. Mild cognitive impairment. N Engl J Med. 2011; 364(23):2227\u201334.","journal-title":"N Engl J Med"},{"issue":"3","key":"3057_CR4","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1038\/nrneurol.2011.2","volume":"7","author":"C Reitz","year":"2011","unstructured":"Reitz C, Brayne C, Mayeux R. Epidemiology of alzheimer disease. Nat Rev Neurol. 2011; 7(3):137.","journal-title":"Nat Rev Neurol"},{"issue":"4","key":"3057_CR5","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1038\/nrn3200","volume":"13","author":"JH Morrison","year":"2012","unstructured":"Morrison JH, Baxter MG. The ageing cortical synapse: hallmarks and implications for cognitive decline. Nat Rev Neurosci. 2012; 13(4):240.","journal-title":"Nat Rev Neurosci"},{"issue":"8","key":"3057_CR6","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1016\/S1474-4422(14)70136-X","volume":"13","author":"S Norton","year":"2014","unstructured":"Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of alzheimer\u2019s disease: an analysis of population-based data. Lancet Neurol. 2014; 13(8):788\u201394.","journal-title":"Lancet Neurol"},{"issue":"19","key":"3057_CR7","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1212\/WNL.0b013e31828726f5","volume":"80","author":"LE Hebert","year":"2013","unstructured":"Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the united states (2010\u20132050) estimated using the 2010 census. Neurology. 2013; 80(19):1778\u201383.","journal-title":"Neurology"},{"issue":"6","key":"3057_CR8","doi-asserted-by":"publisher","first-page":"65841","DOI":"10.1371\/journal.pone.0065841","volume":"8","author":"DM Lipnicki","year":"2013","unstructured":"Lipnicki DM, Sachdev PS, Crawford J, Reppermund S, Kochan NA, Trollor JN, Draper B, Slavin MJ, Kang K, Lux O, et al.Risk factors for late-life cognitive decline and variation with age and sex in the sydney memory and ageing study. PloS ONE. 2013; 8(6):65841.","journal-title":"PloS ONE"},{"issue":"2","key":"3057_CR9","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1111\/psyg.12083","volume":"15","author":"ME Lenehan","year":"2015","unstructured":"Lenehan ME, Summers MJ, Saunders NL, Summers JJ, Vickers JC. Relationship between education and age-related cognitive decline: A review of recent research. Psychogeriatrics. 2015; 15(2):154\u201362.","journal-title":"Psychogeriatrics"},{"issue":"3","key":"3057_CR10","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1109\/TCBB.2015.2459692","volume":"13","author":"B Teng","year":"2016","unstructured":"Teng B, Yang C, Liu J, Cai Z, Wan X. Exploring the genetic patterns of complex diseases via the integrative genome-wide approach. IEEE\/ACM Trans Comput Biol Bioinforma. 2016; 13(3):557\u201364.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"issue":"2","key":"3057_CR11","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/TNB.2015.2403871","volume":"14","author":"D Xu","year":"2015","unstructured":"Xu D, Cai Z, Liu K, Zeng X, Ouyang Y, Dai C, Hou A, Cheng D, Li J. Design and simulation of proportional biological operational mu-circuit. IEEE Trans Nanobiosci. 2015; 14(2):248\u201353.","journal-title":"IEEE Trans Nanobiosci"},{"key":"3057_CR12","unstructured":"Yang Y, Dai C, Cai Z, Hou A, Cheng D, Wu G, Li J, Cui J, Xu D. Modeling and simulation of conjugated linoleic acid biosynthesis pathway. IEEE Trans Nanobiosci. 2018. https:\/\/cis.ieee.org\/ieee-transactions-on-nanobioscience.html."},{"issue":"10","key":"3057_CR13","doi-asserted-by":"publisher","first-page":"1000949","DOI":"10.1371\/journal.pcbi.1000949","volume":"6","author":"Z Cai","year":"2010","unstructured":"Cai Z, Zhang T, Wan X-F. A computational framework for influenza antigenic cartography. PLoS Comput Biol. 2010; 6(10):1000949.","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"3057_CR14","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TCBB.2016.2527648","volume":"14","author":"X Guo","year":"2017","unstructured":"Guo X, Zhang J, Cai Z, Du D-Z, Pan Y. Searching genome-wide multi-locus associations for multiple diseases based on bayesian inference. IEEE\/ACM Trans Comput Biol Bioinforma (TCBB). 2017; 14(3):600\u201310.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma (TCBB)"},{"key":"3057_CR15","volume-title":"Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom), 2016 IEEE International Conferences On","author":"M Han","year":"2016","unstructured":"Han M, Liang Y, Duan Z, Wang Y. Mining public business knowledge: A case study in sec\u2019s edgar. In: Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom), 2016 IEEE International Conferences On. Atlanta: IEEE: 2016. p. 393\u2013400."},{"key":"3057_CR16","doi-asserted-by":"publisher","unstructured":"Zhou Y, Han M, Liu L, He JS, Wang Y. Deep learning approach for cyberattack detection. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE: 2018. p. 262\u20137. https:\/\/doi.org\/10.1109\/infcomw.2018.8407032.","DOI":"10.1109\/infcomw.2018.8407032"},{"key":"3057_CR17","volume-title":"International Computing and Combinatorics Conference","author":"M Han","year":"2013","unstructured":"Han M, Yan M, Li J, Ji S, Li Y. Generating uncertain networks based on historical network snapshots. In: International Computing and Combinatorics Conference. Berlin, Heidelberg: Springer: 2013. p. 747\u201358."},{"key":"3057_CR18","doi-asserted-by":"publisher","unstructured":"Albinali H, Han M, Wang J, Gao H, Li Y. The roles of social network mavens. In: Mobile Ad-Hoc and Sensor Networks (MSN), 2016 12th International Conference On. IEEE: 2016. p. 1\u20138. https:\/\/doi.org\/10.1109\/msn.2016.009.","DOI":"10.1109\/msn.2016.009"},{"issue":"3\u20134","key":"3057_CR19","first-page":"165","volume":"15","author":"M Han","year":"2017","unstructured":"Han M, Duan Z, Ai C, Lybarger FW, Li Y, Bourgeois AG. Time constraint influence maximization algorithm in the age of big data. Int J Comput Sci Eng. 2017; 15(3\u20134):165\u201375.","journal-title":"Int J Comput Sci Eng"},{"issue":"1","key":"3057_CR20","doi-asserted-by":"publisher","first-page":"47","DOI":"10.26599\/BDMA.2018.9020005","volume":"1","author":"B Zhou","year":"2018","unstructured":"Zhou B, Li J, Wang X, Gu Y, Xu L, Hu Y, Zhu L. Online internet traffic monitoring system using spark streaming. Big Data Min Anal. 2018; 1(1):47\u201356.","journal-title":"Big Data Min Anal"},{"key":"3057_CR21","doi-asserted-by":"publisher","first-page":"13466","DOI":"10.1109\/ACCESS.2018.2805464","volume":"6","author":"M Han","year":"2018","unstructured":"Han M, Li L, Xie Y, Wang J, Duan Z, Li J, Yan M. Cognitive approach for location privacy protection. IEEE Access. 2018; 6:13466\u201377.","journal-title":"IEEE Access"},{"key":"3057_CR22","doi-asserted-by":"publisher","unstructured":"Liu L, Han M, Wang Y, Zhou Y. Understanding data breach: A visualization aspect. In: International Conference on Wireless Algorithms, Systems, and Applications. Springer: 2018. p. 883\u201392. https:\/\/doi.org\/10.1007\/978-3-319-94268-1_81.","DOI":"10.1007\/978-3-319-94268-1_81"},{"key":"3057_CR23","unstructured":"Kashyap H, Ahmed HA, Hoque N, Roy S, Bhattacharyya DK. Big data analytics in bioinformatics: A machine learning perspective. arXiv preprint. 2015. arXiv:1506.05101."},{"key":"3057_CR24","doi-asserted-by":"publisher","unstructured":"Zheng J, Liang M, Ekstrom A, Ge L, Yu W, Hsieh F. On association study of scalp eeg data channels under different circumstances. In: International Conference on Wireless Algorithms, Systems, and Applications. Springer: 2018. p. 683\u201395. https:\/\/doi.org\/10.1007\/978-3-319-94268-1_56.","DOI":"10.1007\/978-3-319-94268-1_56"},{"key":"3057_CR25","doi-asserted-by":"publisher","unstructured":"Liu L, Han M, Zhou Y, Wang Y. Lstm recurrent neural networks for influenza trends prediction. In: International Symposium on Bioinformatics Research and Applications. Springer: 2018. p. 259\u201364. https:\/\/doi.org\/10.1007\/978-3-319-94968-0_25.","DOI":"10.1007\/978-3-319-94968-0_25"},{"issue":"1","key":"3057_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.26599\/BDMA.2018.9020001","volume":"1","author":"J Liu","year":"2018","unstructured":"Liu J, Pan Y, Li M, Chen Z, Tang L, Lu C, Wang J. Applications of deep learning to mri images: a survey. Big Data Min Anal. 2018; 1(1):1\u201318.","journal-title":"Big Data Min Anal"},{"key":"3057_CR27","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. Smote: synthetic minority over-sampling technique. J Artif Intell Res. 2002; 16:321\u201357.","journal-title":"J Artif Intell Res"},{"issue":"1","key":"3057_CR28","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TSMCB.2008.2002909","volume":"39","author":"Y Tang","year":"2009","unstructured":"Tang Y, Zhang Y-Q, Chawla NV, Krasser S. Svms modeling for highly imbalanced classification. IEEE Trans Syst Man Cybern Part B (Cybern). 2009; 39(1):281\u20138.","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"key":"3057_CR29","unstructured":"Dittman DJ, Khoshgoftaar TM, Wald R, Napolitano A. Comparison of data sampling approaches for imbalanced bioinformatics data. In: FLAIRS Conference. Association for the Advancement of Artificial Intelligence (AAAI): 2014. http:\/\/www.aaai.org."},{"issue":"4","key":"3057_CR30","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal. 2002; 38(4):367\u201378.","journal-title":"Comput Stat Data Anal"},{"key":"3057_CR31","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. ACM: 2016. p. 785\u201394. https:\/\/www.acm.org. https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"3057_CR32","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001; 45(1):5\u201332.","journal-title":"Mach Learn"},{"issue":"2","key":"3057_CR33","first-page":"83","volume":"27","author":"T Hastie","year":"2005","unstructured":"Hastie T, Tibshirani R, Friedman J, Franklin J. The elements of statistical learning: data mining, inference and prediction. Math Intell. 2005; 27(2):83\u20135.","journal-title":"Math Intell"},{"issue":"2","key":"3057_CR34","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/BF02985802","volume":"27","author":"J Franklin","year":"2005","unstructured":"Franklin J. The elements of statistical learning: data mining, inference and prediction. Math Intell. 2005; 27(2):83\u20135.","journal-title":"Math Intell"},{"key":"3057_CR35","doi-asserted-by":"publisher","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. Association for Computing Machinery (ACM): 2012. p. 1097\u2013105. https:\/\/doi.org\/10.1145\/3065386.","DOI":"10.1145\/3065386"},{"issue":"1","key":"3057_CR36","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.jcjd.2016.07.003","volume":"41","author":"J Li","year":"2017","unstructured":"Li J, Cesari M, Liu F, Dong B, Vellas B. Effects of diabetes mellitus on cognitive decline in patients with alzheimer disease: a systematic review. Can J Diabetes. 2017; 41(1):114\u20139.","journal-title":"Can J Diabetes"},{"issue":"320","key":"3057_CR37","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1080\/01621459.1967.10500922","volume":"62","author":"JA Hartigan","year":"1967","unstructured":"Hartigan JA. Representation of similarity matrices by trees. J Am Stat Assoc. 1967; 62(320):1140\u201358.","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"3057_CR38","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/BF01897163","volume":"5","author":"GW Milligan","year":"1988","unstructured":"Milligan GW, Cooper MC. A study of standardization of variables in cluster analysis. J Classif. 1988; 5(2):181\u2013204.","journal-title":"J Classif"},{"issue":"2","key":"3057_CR39","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1111\/1467-9868.00293","volume":"63","author":"R Tibshirani","year":"2001","unstructured":"Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B (Stat Methodol). 2001; 63(2):411\u201323.","journal-title":"J R Stat Soc Ser B (Stat Methodol)"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3057-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3057-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3057-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T00:06:36Z","timestamp":1608681996000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3057-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":39,"journal-issue":{"issue":"S15","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3057"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3057-1","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"21 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2019","order":3,"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"}}],"article-number":"481"}}