{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T07:57:27Z","timestamp":1726041447768},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030301453"},{"type":"electronic","value":"9783030301460"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30146-0_23","type":"book-chapter","created":{"date-parts":[[2019,8,17]],"date-time":"2019-08-17T22:02:28Z","timestamp":1566079348000},"page":"334-349","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integration of Machine Learning Techniques as Auxiliary Diagnosis of Inherited Metabolic Disorders: Promising Experience with Newborn Screening Data"],"prefix":"10.1007","author":[{"given":"Bo","family":"Lin","sequence":"first","affiliation":[]},{"given":"Jianwei","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Shu","sequence":"additional","affiliation":[]},{"given":"Shuiguang","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[]},{"given":"Pingping","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Rulai","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Calton","family":"Pu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,18]]},"reference":[{"issue":"1","key":"23_CR1","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GE Batista","year":"2004","unstructured":"Batista, G.E., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor. Newslett. 6(1), 20\u201329 (2004)","journal-title":"ACM SIGKDD Explor. Newslett."},{"issue":"2","key":"23_CR2","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.jbi.2004.08.009","volume":"38","author":"C Baumgartner","year":"2005","unstructured":"Baumgartner, C., B\u00f6hm, C., Baumgartner, D.: Modelling of classification rules on metabolic patterns including machine learning and expert knowledge. J. Biomed. Inform. 38(2), 89\u201398 (2005)","journal-title":"J. Biomed. Inform."},{"issue":"17","key":"23_CR3","doi-asserted-by":"publisher","first-page":"2985","DOI":"10.1093\/bioinformatics\/bth343","volume":"20","author":"C Baumgartner","year":"2004","unstructured":"Baumgartner, C., et al.: Supervised machine learning techniques for the classification of metabolic disorders in newborns. Bioinformatics 20(17), 2985\u20132996 (2004)","journal-title":"Bioinformatics"},{"issue":"2","key":"23_CR4","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.jbi.2010.12.001","volume":"44","author":"T Bulcke Van den","year":"2011","unstructured":"Van den Bulcke, T., et al.: Data mining methods for classification of medium-chain Acyl-CoA dehydrogenase deficiency (MCADD) using non-derivatized tandem MS neonatal screening data. J. Biomed. Inform. 44(2), 319\u2013325 (2011)","journal-title":"J. Biomed. Inform."},{"key":"23_CR5","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1111\/j.1651-2227.1999.tb01156.x","volume":"88","author":"D Chace","year":"1999","unstructured":"Chace, D., DiPerna, J., Naylor, E.: Laboratory integration and utilization of tandem mass spectrometry in neonatal screening: a model for clinical mass spectrometry in the next millennium. Acta Paediatr. 88, 45\u201347 (1999)","journal-title":"Acta Paediatr."},{"key":"23_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"issue":"22","key":"23_CR7","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402\u20132410 (2016)","journal-title":"JAMA"},{"issue":"6","key":"23_CR8","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1542\/peds.2005-2294","volume":"117","author":"EA Gurian","year":"2006","unstructured":"Gurian, E.A., Kinnamon, D.D., Henry, J.J., Waisbren, S.E.: Expanded newborn screening for biochemical disorders: the effect of a false-positive result. Pediatrics 117(6), 1915\u20131921 (2006)","journal-title":"Pediatrics"},{"key":"23_CR9","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157\u20131182 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"23_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/11538059_91","volume-title":"Advances in Intelligent Computing","author":"H Han","year":"2005","unstructured":"Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878\u2013887. Springer, Heidelberg (2005). \n                    https:\/\/doi.org\/10.1007\/11538059_91"},{"issue":"7641","key":"23_CR11","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1038\/nature21369","volume":"542","author":"HC Hazlett","year":"2017","unstructured":"Hazlett, H.C., et al.: Early brain development in infants at high risk for autism spectrum disorder. Nature 542(7641), 348 (2017)","journal-title":"Nature"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Iba, W., Langley, P.: Induction of one-level decision trees. In: Machine Learning Proceedings 1992, pp. 233\u2013240. Elsevier (1992)","DOI":"10.1016\/B978-1-55860-247-2.50035-8"},{"key":"23_CR13","unstructured":"Kubat, M., Matwin, S., et al.: Addressing the curse of imbalanced training sets: one-sided selection. In: International Conference on Machine Learning, Nashville, USA, vol. 97, pp. 179\u2013186 (1997)"},{"issue":"1","key":"23_CR14","first-page":"559","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(1), 559\u2013563 (2017)","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"23_CR15","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/BF01799385","volume":"13","author":"D Millington","year":"1990","unstructured":"Millington, D., Kodo, N., Norwood, D., Roe, C.: Tandem mass spectrometry: a new method for acylcarnitine profiling with potential for neonatal screening for inborn errors of metabolism. J. Inherit. Metab. Dis. 13(3), 321\u2013324 (1990)","journal-title":"J. Inherit. Metab. Dis."},{"key":"23_CR16","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"23_CR17","first-page":"769","volume":"6","author":"I Tomek","year":"1976","unstructured":"Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. 6, 769\u2013772 (1976)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"5","key":"23_CR18","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1542\/peds.112.5.1005","volume":"112","author":"LN Venditti","year":"2003","unstructured":"Venditti, L.N., et al.: Newborn screening by tandem mass spectrometry for medium-chain Acyl-CoA dehydrogenase deficiency: a cost-effectiveness analysis. Pediatrics 112(5), 1005\u20131015 (2003)","journal-title":"Pediatrics"},{"key":"23_CR19","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","volume":"3","author":"DL Wilson","year":"1972","unstructured":"Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. 3, 408\u2013421 (1972)","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Collaborative Computing: Networking, Applications and Worksharing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30146-0_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,17]],"date-time":"2019-08-17T22:05:56Z","timestamp":1566079556000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30146-0_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030301453","9783030301460"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30146-0_23","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"18 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CollaborateCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Collaborative Computing: Networking, Applications and Worksharing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"colcom2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/collaboratecom.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}