{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T22:08:24Z","timestamp":1779228504874,"version":"3.51.4"},"reference-count":25,"publisher":"Walter de Gruyter GmbH","issue":"3","license":[{"start":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T00:00:00Z","timestamp":1525910400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,9,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various \u2013 omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.<\/jats:p>","DOI":"10.1515\/jib-2017-0030","type":"journal-article","created":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T22:15:43Z","timestamp":1525990543000},"source":"Crossref","is-referenced-by-count":262,"title":["Big Data Analytics in Medicine and Healthcare"],"prefix":"10.1515","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8356-1203","authenticated-orcid":false,"given":"Blagoj","family":"Ristevski","sequence":"first","affiliation":[{"name":"\u201cSt. Kliment Ohridski\u201d University \u2013 Bitola, Faculty of Information and Communication Technologies , ul. Partizanska bb , 7000 Bitola , Republic of Macedonia"}]},{"given":"Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics , College of Life Sciences, Zhejiang University Zijingang Campus , Hangzhou , P.R. China"}]}],"member":"374","published-online":{"date-parts":[[2018,5,10]]},"reference":[{"key":"2023033119204011168_j_jib-2017-0030_ref_001_w2aab3b7b1b1b6b1ab1b9b1Aa","doi-asserted-by":"crossref","unstructured":"Yang C, Li C, Wang Q, Chung D, Zhao H. Implications of pleiotropy: challenges and opportunities for mining big data in biomedicine. Front Genet 2015;6:229.26175753","DOI":"10.3389\/fgene.2015.00229"},{"key":"2023033119204011168_j_jib-2017-0030_ref_002_w2aab3b7b1b1b6b1ab1b9b2Aa","doi-asserted-by":"crossref","unstructured":"Viceconti M, Hunter P, Hose R. Big data, big knowledge: big data for personalized healthcare. IEEE J Biomed Health Inform 2015;19:1209\u201315.2621886710.1109\/JBHI.2015.2406883","DOI":"10.1109\/JBHI.2015.2406883"},{"key":"2023033119204011168_j_jib-2017-0030_ref_003_w2aab3b7b1b1b6b1ab1b9b3Aa","doi-asserted-by":"crossref","unstructured":"Kankanhalli A, Hahn J, Tan S, Gao G. Big data and analytics in healthcare: introduction to the special section. Inform Syst Front 2016;18:233\u20135.10.1007\/s10796-016-9641-2","DOI":"10.1007\/s10796-016-9641-2"},{"key":"2023033119204011168_j_jib-2017-0030_ref_004_w2aab3b7b1b1b6b1ab1b9b4Aa","doi-asserted-by":"crossref","unstructured":"Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inform Sci Syst 2014;2:3.10.1186\/2047-2501-2-3","DOI":"10.1186\/2047-2501-2-3"},{"key":"2023033119204011168_j_jib-2017-0030_ref_005_w2aab3b7b1b1b6b1ab1b9b5Aa","doi-asserted-by":"crossref","unstructured":"Wu PY, Cheng CW, Kaddi CD, Venugopalan J, Hoffman R, Wang MD. \u2013Omic and Electronic Health Record Big Data Analytics for Precision Medicine. IEEE Trans Biomed Eng 2017;64:263\u201373.2774047010.1109\/TBME.2016.2573285","DOI":"10.1109\/TBME.2016.2573285"},{"key":"2023033119204011168_j_jib-2017-0030_ref_006_w2aab3b7b1b1b6b1ab1b9b6Aa","doi-asserted-by":"crossref","unstructured":"Wang Y, Kung LA, Wang WY, Cegielski CG. An integrated big data analytics-enabled transformation model: application to health care. Inf Manag 2017;55:64\u201379.","DOI":"10.1016\/j.im.2017.04.001"},{"key":"2023033119204011168_j_jib-2017-0030_ref_007_w2aab3b7b1b1b6b1ab1b9b7Aa","doi-asserted-by":"crossref","unstructured":"El-Gayar O, Timsina P. Opportunities for business intelligence and big data analytics in evidence based medicine. In: System Sciences (HICSS), 2014 47th Hawaii international conference on 2014:749\u201357.","DOI":"10.1109\/HICSS.2014.100"},{"key":"2023033119204011168_j_jib-2017-0030_ref_008_w2aab3b7b1b1b6b1ab1b9b8Aa","doi-asserted-by":"crossref","unstructured":"Gu D, Li J, Li X, Liang C. Visualizing the knowledge structure and evolution of big data research in healthcare informatics. Int J Med Inform 2017;98:22\u201332.2803440910.1016\/j.ijmedinf.2016.11.006","DOI":"10.1016\/j.ijmedinf.2016.11.006"},{"key":"2023033119204011168_j_jib-2017-0030_ref_009_w2aab3b7b1b1b6b1ab1b9b9Aa","doi-asserted-by":"crossref","unstructured":"Gligorijevi\u0107 V, Malod\u2010Dognin N, Pr\u017eulj N. Integrative methods for analyzing big data in precision medicine. Proteomics 2016;16:741\u201358.2667781710.1002\/pmic.201500396","DOI":"10.1002\/pmic.201500396"},{"key":"2023033119204011168_j_jib-2017-0030_ref_010_w2aab3b7b1b1b6b1ab1b9c10Aa","doi-asserted-by":"crossref","unstructured":"Luo J, Wu M, Gopukumar D, Zhao Y. Big data application in biomedical research and health care: a literature review. Biomed Inform Insights 2016;8:1.26843812","DOI":"10.4137\/BII.S31559"},{"key":"2023033119204011168_j_jib-2017-0030_ref_011_w2aab3b7b1b1b6b1ab1b9c11Aa","doi-asserted-by":"crossref","unstructured":"Gaitanou P, Garoufallou E, Balatsoukas P. The effectiveness of big data in health care: a systematic review. In: Metadata and semantics research. 2014:141\u201353.","DOI":"10.1007\/978-3-319-13674-5_14"},{"key":"2023033119204011168_j_jib-2017-0030_ref_012_w2aab3b7b1b1b6b1ab1b9c12Aa","doi-asserted-by":"crossref","unstructured":"Lillo-Castellano JM, Mora-Jimenez I, Santiago-Mozos R, Chavarria-Asso F, Cano-Gonz\u00e1lez A, Garc\u00eda-Alberola A, et al. Symmetrical compression distance for arrhythmia discrimination in cloud-based big-data services. IEEE J Biomed Health Inform 2015;19:1253\u201363.10.1109\/JBHI.2015.241217525823046","DOI":"10.1109\/JBHI.2015.2412175"},{"key":"2023033119204011168_j_jib-2017-0030_ref_013_w2aab3b7b1b1b6b1ab1b9c13Aa","doi-asserted-by":"crossref","unstructured":"Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang GZ. Big data for health. IEEE J Biomed Health Inform 2015;19:1193\u20131208.10.1109\/JBHI.2015.2450362","DOI":"10.1109\/JBHI.2015.2450362"},{"key":"2023033119204011168_j_jib-2017-0030_ref_014_w2aab3b7b1b1b6b1ab1b9c14Aa","doi-asserted-by":"crossref","unstructured":"Archenaa J, Anita EM. A survey of big data analytics in healthcare and government. Procedia Comput Sci 2015;50:408\u201313.10.1016\/j.procs.2015.04.021","DOI":"10.1016\/j.procs.2015.04.021"},{"key":"2023033119204011168_j_jib-2017-0030_ref_015_w2aab3b7b1b1b6b1ab1b9c15Aa","unstructured":"Borne K. Top 10 big data challenges \u2013 a serious look at 10 big data V\u2019s. MAPR, 2014:NO4, 80."},{"key":"2023033119204011168_j_jib-2017-0030_ref_016_w2aab3b7b1b1b6b1ab1b9c16Aa","unstructured":"Hermon R, Williams PA. Big data in healthcare: what is it used for? In: Australian Ehealth Informatics and Security Conference. 2014:40\u20139."},{"key":"2023033119204011168_j_jib-2017-0030_ref_017_w2aab3b7b1b1b6b1ab1b9c17Aa","doi-asserted-by":"crossref","unstructured":"Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters. Commun ACM 2008;51:107\u201313.10.1145\/1327452.1327492","DOI":"10.1145\/1327452.1327492"},{"key":"2023033119204011168_j_jib-2017-0030_ref_018_w2aab3b7b1b1b6b1ab1b9c18Aa","doi-asserted-by":"crossref","unstructured":"Trifonova OP, Il\u2019in VA, Kolker EV, Lisitsa AV. Big data in biology and medicine. Acta Naturae 2013;5:13\u20136.24303199","DOI":"10.32607\/20758251-2013-5-3-13-16"},{"key":"2023033119204011168_j_jib-2017-0030_ref_019_w2aab3b7b1b1b6b1ab1b9c19Aa","doi-asserted-by":"crossref","unstructured":"Agarwal M, Adhil M, Talukder AK. Multi-omics multi-scale big data analytics for cancer genomics. In: International Conference on Big Data Analytics. Cham, Switzerland:  Springer International Publishing; 2015:228\u201343.","DOI":"10.1007\/978-3-319-27057-9_16"},{"key":"2023033119204011168_j_jib-2017-0030_ref_020_w2aab3b7b1b1b6b1ab1b9c20Aa","doi-asserted-by":"crossref","unstructured":"He KY, Ge D, He MM. Big data analytics for genomic medicine. Int J Mol Sci 2017;18:412.10.3390\/ijms18020412","DOI":"10.3390\/ijms18020412"},{"key":"2023033119204011168_j_jib-2017-0030_ref_021_w2aab3b7b1b1b6b1ab1b9c21Aa","doi-asserted-by":"crossref","unstructured":"Tan SL, Gao G, Koch S. Big data and analytics in healthcare. Methods Inf Med 2015;54:546\u20137.10.3414\/ME15-06-100126577624","DOI":"10.3414\/ME15-06-1001"},{"key":"2023033119204011168_j_jib-2017-0030_ref_022_w2aab3b7b1b1b6b1ab1b9c22Aa","doi-asserted-by":"crossref","unstructured":"Dinov ID, Heavner B, Tang M, Glusman G, Chard K, Darcy M, et al. Predictive big data analytics: a study of Parkinson\u2019s disease using large, complex, heterogeneous, incongruent, multi-source and incomplete observations. PLoS One 2016;11:e0157077.27494614","DOI":"10.1371\/journal.pone.0157077"},{"key":"2023033119204011168_j_jib-2017-0030_ref_023_w2aab3b7b1b1b6b1ab1b9c23Aa","doi-asserted-by":"crossref","unstructured":"Costa FF. Big data in biomedicine. Drug Discov Today 2014;19:433\u201340.10.1016\/j.drudis.2013.10.01224183925","DOI":"10.1016\/j.drudis.2013.10.012"},{"key":"2023033119204011168_j_jib-2017-0030_ref_024_w2aab3b7b1b1b6b1ab1b9c24Aa","doi-asserted-by":"crossref","unstructured":"Yao Q, Tian Y, Li PF, Tian LL, Qian YM, Li JS. Design and development of a medical big data processing system based on Hadoop. J Med Syst 2015;39:23.10.1007\/s10916-015-0220-825666927","DOI":"10.1007\/s10916-015-0220-8"},{"key":"2023033119204011168_j_jib-2017-0030_ref_025_w2aab3b7b1b1b6b1ab1b9c25Aa","doi-asserted-by":"crossref","unstructured":"Kambatla K, Kollias G, Kumar V, Grama A. Trends in big data analytics. J Parallel Distrib Comput 2014;74:2561\u201373.10.1016\/j.jpdc.2014.01.003","DOI":"10.1016\/j.jpdc.2014.01.003"}],"container-title":["Journal of Integrative Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/jib.2018.15.issue-3\/jib-2017-0030\/jib-2017-0030.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/jib-2017-0030\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/jib-2017-0030\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T06:25:39Z","timestamp":1680330339000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/jib-2017-0030\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,10]]},"references-count":25,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,8,7]]},"published-print":{"date-parts":[[2018,9,25]]}},"alternative-id":["10.1515\/jib-2017-0030"],"URL":"https:\/\/doi.org\/10.1515\/jib-2017-0030","relation":{},"ISSN":["1613-4516"],"issn-type":[{"value":"1613-4516","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,10]]},"article-number":"20170030"}}