{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:51:28Z","timestamp":1771703488884,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2018,11,26]],"date-time":"2018-11-26T00:00:00Z","timestamp":1543190400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61472232"],"award-info":[{"award-number":["61472232"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61572300"],"award-info":[{"award-number":["61572300"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2016FB13"],"award-info":[{"award-number":["ZR2016FB13"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2019,4]]},"DOI":"10.1007\/s11517-018-1930-0","type":"journal-article","created":{"date-parts":[[2018,11,26]],"date-time":"2018-11-26T15:36:12Z","timestamp":1543246572000},"page":"901-912","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["A reliable method for colorectal cancer prediction based on feature selection and support vector machine"],"prefix":"10.1007","volume":"57","author":[{"given":"Dandan","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yuanjie","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Yanlin","family":"He","sequence":"additional","affiliation":[]},{"given":"Dianjie","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Lyu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,26]]},"reference":[{"key":"1930_CR1","first-page":"1","volume":"9","author":"SA Zadeh","year":"2017","unstructured":"Zadeh SA, Sj SMC, Mohammadi Z (2017) A novel and reliable computational intelligence system for breast cancer detection. Germ J Med Biol Eng Comp 9:1\u201312","journal-title":"Germ J Med Biol Eng Comp"},{"key":"1930_CR2","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1007\/s11517-015-1360-1","volume":"54","author":"JK Pal","year":"2015","unstructured":"Pal JK, Ray SS, Pal SK (2015) Identifying relevant group of miRNAs in cancer using fuzzy mutual information. Germ J Medical & Biological Engineering & Computing 54:701\u2013710","journal-title":"Germ J Medical & Biological Engineering & Computing"},{"key":"1930_CR3","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1053\/j.gastro.2010.01.057","volume":"138","author":"AT Chan","year":"2010","unstructured":"Chan AT, Giovannucci EL (2010) Primary prevention of colorectal cancer. J Gastroenterol 138:2029\u20132043","journal-title":"J Gastroenterol"},{"key":"1930_CR4","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/nri2891","volume":"11","author":"M Saleh","year":"2010","unstructured":"Saleh M, Trinchieri G (2010) Innate immune mechanisms of colitis and colitis-associated colorectal cancer. N Eng J Nature Rev Immunol 11:9\u201320","journal-title":"N Eng J Nature Rev Immunol"},{"key":"1930_CR5","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1146\/annurev-micro-102215-095513","volume":"70","author":"CA Brennan","year":"2016","unstructured":"Brennan CA, Garrett WS (2016) Gut microbiota, inflammation, and colorectal cancer. US J Ann Rev Microbiol 70:395\u2013411","journal-title":"US J Ann Rev Microbiol"},{"key":"1930_CR6","unstructured":"Chatterjee S, Dey N, Shi F, Ashour AS et al (2017) Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data. Germ J Med Biol Eng Comp:1\u201312"},{"key":"1930_CR7","first-page":"15","volume":"13","author":"A Ay","year":"2014","unstructured":"Ay A, Gong D, Kahveci T (2014) Network-based prediction of cancer under genetic storm. J Cancer Inform 13:15\u201331","journal-title":"J Cancer Inform"},{"key":"1930_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12863-014-0153-0","volume":"16","author":"KJ Jung","year":"2015","unstructured":"Jung KJ, Won D, Jeon C et al (2015) A colorectal cancer prediction model using traditional and genetic risk scores in Koreans. N Eng J BMC Genet 16:1\u20137","journal-title":"N Eng J BMC Genet"},{"key":"1930_CR9","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1186\/s12916-016-0668-5","volume":"14","author":"J Cubiella","year":"2016","unstructured":"Cubiella J, Vega P, Salve M et al (2016) Development and external validation of a fecal immunochemical test-based prediction model for colorectal cancer detection in symptomatic patients. J BMC Med 14:128\u2013140","journal-title":"J BMC Med"},{"key":"1930_CR10","doi-asserted-by":"publisher","first-page":"175","DOI":"10.2217\/epi.14.77","volume":"7","author":"F Copped\u00e8","year":"2015","unstructured":"Copped\u00e8 F, Grossi E, Lopomo A et al (2015) Application of artificial neural networks to link genetic and environmental factors to DNA methylation in colorectal cancer. N Eng J Epigenomics 7:175\u2013186","journal-title":"N Eng J Epigenomics"},{"key":"1930_CR11","first-page":"982","volume":"8","author":"Y Peng","year":"2015","unstructured":"Peng Y, Zhai Z, Li Z et al (2015) Role of blood tumor markers in predicting metastasis and local recurrence after curative resection of colon cancer. J Int J Clin Exp Med 8:982\u2013990","journal-title":"J Int J Clin Exp Med"},{"key":"1930_CR12","doi-asserted-by":"publisher","first-page":"e0153778","DOI":"10.1371\/journal.pone.0153778","volume":"11","author":"M Juan","year":"2016","unstructured":"Juan M, Philippe W, Nermin G et al (2016) An original stepwise multilevel logistic regression analysis of discriminatory accuracy: the case of neighborhoods and health. US J Plos One 11:e0153778","journal-title":"US J Plos One"},{"key":"1930_CR13","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. US J Mach Learn 46:389\u2013422","journal-title":"US J Mach Learn"},{"key":"1930_CR14","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1007\/s10044-014-0375-9","volume":"18","author":"F Ahmad","year":"2015","unstructured":"Ahmad F, Mat Isa NA, Hussain Z, Osman MK, Sulaiman SN (2015) GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer. J Pattern Analysis Appl 18:861\u2013870","journal-title":"J Pattern Analysis Appl"},{"key":"1930_CR15","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/S0014-5793(03)01275-4","volume":"555","author":"S Peng","year":"2003","unstructured":"Peng S, Xu Q, Ling XB, Peng X, du W, Chen L (2003) Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. J Febs Lett 555:358\u2013362","journal-title":"J Febs Lett"},{"key":"1930_CR16","doi-asserted-by":"crossref","unstructured":"Liu W, Zheng W L, Lu B L (2016) Emotion recognition using multimodal deep learning","DOI":"10.1007\/978-3-319-46672-9_58"},{"key":"1930_CR17","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.ins.2014.05.042","volume":"282","author":"V Bol\u00f3n-Canedo","year":"2014","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A, Ben\u00edtez JM, Herrera F (2014) A review of microarray datasets and applied feature selection methods. US J Inform Sci 282:111\u2013135","journal-title":"US J Inform Sci"},{"key":"1930_CR18","doi-asserted-by":"publisher","first-page":"2429","DOI":"10.1093\/bioinformatics\/bth267","volume":"20","author":"T Li","year":"2004","unstructured":"Li T, Zhang C, Ogihara M (2004) A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. N Eng J Bioinform 20:2429\u20132437","journal-title":"N Eng J Bioinform"},{"key":"1930_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. J ACM Trans Intel Systems & Technol 2:1\u201327","journal-title":"J ACM Trans Intel Systems & Technol"},{"key":"1930_CR20","doi-asserted-by":"publisher","first-page":"97","DOI":"10.17555\/jvc.2016.04.33.2.97","volume":"33","author":"SI Park","year":"2016","unstructured":"Park SI, Tae-Ho O (2016) Application of receiver operating characteristic (ROC) curve for evaluation of diagnostic test performance. J Vet Clin 33:97\u2013108","journal-title":"J Vet Clin"},{"key":"1930_CR21","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1007\/s11517-013-1091-0","volume":"51","author":"KA Kim","year":"2013","unstructured":"Kim KA, Choi JY, Yoo TK, Kim SK, Chung KS, Kim DW (2013) Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. Germ J Med Biol Eng Comp 51:1059\u20131067","journal-title":"Germ J Med Biol Eng Comp"},{"key":"1930_CR22","doi-asserted-by":"publisher","unstructured":"Chowdhury A R, Chatterjee T, Banerjee S (2018) A random forest classifier-based approach in the detection of abnormalities in the retina. Germ J Med Biol Eng Comp Available at doi:\n                    https:\/\/doi.org\/10.1007\/s11517-018-1878-0","DOI":"10.1007\/s11517-018-1878-0"},{"issue":"2\u20133","key":"1930_CR23","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s11517-015-1321-8","volume":"54","author":"H Zhang","year":"2016","unstructured":"Zhang H, Yu P, Xiang ML, Li XB, Kong WB, Ma JY, Wang JL, Zhang JP, Zhang J (2016) Prediction of drug-induced eosinophilia adverse effect by using SVM and na\u00efve Bayesian approaches. Germ J Med Biol Eng Comp 54(2\u20133):361\u2013369","journal-title":"Germ J Med Biol Eng Comp"},{"key":"1930_CR24","doi-asserted-by":"crossref","unstructured":"Zhang S, Li X, Zong M et al (2018) Efficient KNN classification with different numbers of nearest neighbors. US J IEEE Trans Neural Networks Learn Systems (99):1\u201312","DOI":"10.1109\/TNNLS.2017.2673241"},{"issue":"4","key":"1930_CR25","doi-asserted-by":"publisher","first-page":"924","DOI":"10.21037\/jtd.2017.03.157","volume":"9","author":"L Bertolaccini","year":"2017","unstructured":"Bertolaccini L, Solli P, Pardolesi A, Pasini A (2017) An overview of the use of artificial neural networks in lung cancer research. J Thorac Dis 9(4):924\u2013931","journal-title":"J Thorac Dis"},{"key":"1930_CR26","first-page":"104","volume":"64","author":"R Siegel","year":"2014","unstructured":"Siegel R, DeSantis C, Jemal A (2014) Colorectal cancer statistics, 2014. J CA: Cancer J Clin 64:104\u2013117","journal-title":"J CA: Cancer J Clin"},{"key":"1930_CR27","doi-asserted-by":"publisher","first-page":"e0120706","DOI":"10.1371\/journal.pone.0120706","volume":"10","author":"J Lee","year":"2015","unstructured":"Lee J, Meyerhardt JA, Giovannucci E, Jeon JY (2015) Association between body mass index and prognosis of colorectal cancer: a meta-analysis of prospective cohort studies. US J PloS one 10:e0120706","journal-title":"US J PloS one"},{"key":"1930_CR28","first-page":"459","volume":"2014","author":"CM Chu","year":"2014","unstructured":"Chu CM, Yao CT, Chang YT et al (2014) Gene expression profiling of colorectal tumors and normal mucosa by microarrays meta-analysis using prediction analysis of microarray, artificial neural network, classification, and regression trees. J Dis Markers 2014:459\u2013462","journal-title":"J Dis Markers"},{"key":"1930_CR29","doi-asserted-by":"publisher","first-page":"6989","DOI":"10.7314\/APJCP.2014.15.17.6989","volume":"15","author":"AV Orang","year":"2014","unstructured":"Orang AV, Barzegari A (2014) MicroRNAs in colorectal cancer: from diagnosis to targeted therapy. Asian Pac J Cancer Prev 15:6989\u20136999","journal-title":"Asian Pac J Cancer Prev"},{"key":"1930_CR30","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.suc.2010.10.010","volume":"91","author":"AK Philip","year":"2011","unstructured":"Philip AK, Lubner MG, Harms B (2011) Computed tomographic colonography. J Surg Clin North Am 91:127\u2013139","journal-title":"J Surg Clin North Am"},{"key":"1930_CR31","doi-asserted-by":"publisher","first-page":"14040","DOI":"10.3748\/wjg.v20.i38.14040","volume":"20","author":"H Zhang","year":"2014","unstructured":"Zhang H, Qi J, Wu YQ, Zhang P, Jiang J, Wang QX, Zhu YQ (2014) Accuracy of early detection of colorectal tumors by stool methylation markers: a meta-analysis. World J Gastroenterol 20:14040\u201314050","journal-title":"World J Gastroenterol"},{"key":"1930_CR32","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1155\/2014\/697103","volume":"28","author":"S Ip","year":"2014","unstructured":"Ip S, Sokoro AA, Kaita L, Ruiz C, McIntyre E, Singh H (2014) Use of fecal occult blood testing in hospitalized patients: results of an audit. Can J Gastroenterol Hepatol 28:489\u2013494","journal-title":"Can J Gastroenterol Hepatol"},{"key":"1930_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12094-016-1500-6","volume":"19","author":"H Li","year":"2017","unstructured":"Li H, Jin Z, Li X et al (2017) Associations between single-nucleotide polymorphisms and inflammatory bowel disease-associated colorectal cancers in inflammatory bowel disease patients: a meta-analysis. J Clinical & Transl Oncol 19:1\u201310","journal-title":"J Clinical & Transl Oncol"},{"key":"1930_CR34","first-page":"1","volume":"15","author":"B Zhang","year":"2016","unstructured":"Zhang B, Liang XL, Gao HY et al (2016) Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis. J Genetics Mol Res 15:1\u201310","journal-title":"J Genetics Mol Res"},{"key":"1930_CR35","doi-asserted-by":"publisher","first-page":"766","DOI":"10.15252\/msb.20145645","volume":"10","author":"G Zeller","year":"2014","unstructured":"Zeller G, Tap J, Voigt AY, Sunagawa S, Kultima JR, Costea PI, Amiot A, Bohm J, Brunetti F, Habermann N, Hercog R, Koch M, Luciani A, Mende DR, Schneider MA, Schrotz-King P, Tournigand C, Tran van Nhieu J, Yamada T, Zimmermann J, Benes V, Kloor M, Ulrich CM, von Knebel Doeberitz M, Sobhani I, Bork P (2014) Potential of fecal microbiota for early-stage detection of colorectal cancer. US J Mol Systems Biol 10:766\u2013783","journal-title":"US J Mol Systems Biol"},{"key":"1930_CR36","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1093\/bioinformatics\/btu170","volume":"30","author":"AM Bolger","year":"2014","unstructured":"Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. N Eng J Bioinformatics 30:2114\u20132120","journal-title":"N Eng J Bioinformatics"},{"key":"1930_CR37","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1038\/nmeth.3589","volume":"12","author":"DT Truong","year":"2015","unstructured":"Truong DT, Franzosa EA, Tickle EL et al (2015) MetaPhlAn2 for enhanced metagenomic taxonomic profiling. US J Nat Methods 12:902\u2013903","journal-title":"US J Nat Methods"},{"key":"1930_CR38","doi-asserted-by":"publisher","first-page":"230","DOI":"10.3390\/antibiotics4030230","volume":"4","author":"C Vincent","year":"2015","unstructured":"Vincent C, Manges AR (2015) Antimicrobial use, human gut microbiota and Clostridium difficile colonization and infection. J Antibiotics 4:230\u2013253","journal-title":"J Antibiotics"},{"key":"1930_CR39","first-page":"2006","volume":"63","author":"D Endesfelder","year":"2014","unstructured":"Endesfelder D, zu-Castell W, Ardissone A et al (2014) Compromised gut microbiota networks in children with anti-islet cell autoimmunity. US J Diabetes DB_131676 63:2006\u20132014","journal-title":"US J Diabetes DB_131676"},{"key":"1930_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10096-016-2778-6","volume":"36","author":"R Gao","year":"2017","unstructured":"Gao R, Gao Z, Huang L, Qin H (2017) Gut microbiota and colorectal cancer. Eur J Eur J Clin Microbiol Infect Dis 36:1\u201313","journal-title":"Eur J Eur J Clin Microbiol Infect Dis"},{"key":"1930_CR41","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1016\/j.cell.2015.11.001","volume":"163","author":"D Zeevi","year":"2015","unstructured":"Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalov\u00e1 L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E (2015) Personalized nutrition by prediction of glycemic responses. US J Cell 163:1079\u20131094","journal-title":"US J Cell"},{"key":"1930_CR42","doi-asserted-by":"crossref","unstructured":"Schmid D, Leitzmann M F (2014) Television viewing and time spent sedentary in relation to cancer risk: a meta-analysis. J Natl Cancer Instit","DOI":"10.1093\/jnci\/dju098"},{"key":"1930_CR43","doi-asserted-by":"publisher","first-page":"739","DOI":"10.3233\/JAD-141086","volume":"43","author":"TL Emmerzaal","year":"2015","unstructured":"Emmerzaal TL, Kiliaan AJ, Gustafson DR (2015) 2003-2013: a decade of body mass index, Alzheimer's disease, and dementia. J. J Alzheimers Dis 43:739\u2013755","journal-title":"J Alzheimers Dis"},{"key":"1930_CR44","first-page":"41","volume":"4","author":"M Alfa-Wali","year":"2015","unstructured":"Alfa-Wali M, Boniface S, Sharma A et al (2015) Metabolic syndrome (Mets) and risk of colorectal cancer (CRC): a systematic review and meta-analysis. J World J Surg Med Radiat Oncol 4:41\u201352","journal-title":"J World J Surg Med Radiat Oncol"},{"key":"1930_CR45","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.chom.2014.02.007","volume":"15","author":"CL Sears","year":"2014","unstructured":"Sears CL, Garrett WS (2014) Microbes, microbiota, and colon cancer. US J Cell Host Microbe 15:317\u2013328","journal-title":"US J Cell Host Microbe"},{"key":"1930_CR46","doi-asserted-by":"crossref","unstructured":"Zhu Q, Jin Z, Wu W, Gao R et al (2014) Analysis of the intestinal lumen microbiota in an animal model of colorectal cancer. US J PLoS One e90849","DOI":"10.1371\/journal.pone.0090849"},{"key":"1930_CR47","doi-asserted-by":"publisher","first-page":"5197","DOI":"10.1016\/j.eswa.2010.10.041","volume":"l38","author":"M Zhao","year":"2011","unstructured":"Zhao M, Fu C, Ji L, Tang K, Zhou M (2011) Feature selection and parameter optimization for support vector machines: a new approach based on genetic algorithm with feature chromosomes. J Expert Syst App l38:5197\u20135204","journal-title":"J Expert Syst App"},{"key":"1930_CR48","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1002\/jmri.22432","volume":"33","author":"X Hu","year":"2011","unstructured":"Hu X, Wong KK, Young GS, Guo L, Wong ST (2011) Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. US J Journal of Magnetic Resonance Imaging 33:296\u2013305","journal-title":"US J Journal of Magnetic Resonance Imaging"},{"key":"1930_CR49","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s11517-015-1321-8","volume":"54","author":"H Zhang","year":"2016","unstructured":"Zhang H, Yu P, Xiang ML, Li XB, Kong WB, Ma JY, Wang JL, Zhang JP, Zhang J (2016) Prediction of drug-induced eosinophilia adverse effect by using SVM and naive Bayesian approaches. Germ J Medical & Biological Engineering & Computing 54:361\u2013370","journal-title":"Germ J Medical & Biological Engineering & Computing"},{"key":"1930_CR50","doi-asserted-by":"crossref","unstructured":"Chen T, Cao Y, Zhang Y et al Random forest in clinical metabolomics for phenotypic discrimination and biomarker selection. Evidence-Based Complementray and Alternative Medicine 2013, 2013:298183\u2013298193","DOI":"10.1155\/2013\/298183"},{"key":"1930_CR51","doi-asserted-by":"crossref","unstructured":"Sacc\u00e1 V, Campolo M, Mirarchi D et al (2018) On the classification of EEG signal by using an SVM based algorithm","DOI":"10.1007\/978-3-319-56904-8_26"},{"key":"1930_CR52","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115\u2013118","journal-title":"Nature"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-018-1930-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11517-018-1930-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-018-1930-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T19:16:22Z","timestamp":1574709382000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11517-018-1930-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,26]]},"references-count":52,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,4]]}},"alternative-id":["1930"],"URL":"https:\/\/doi.org\/10.1007\/s11517-018-1930-0","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,26]]},"assertion":[{"value":"16 December 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}