{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T15:04:44Z","timestamp":1747062284027,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030440930"},{"type":"electronic","value":"9783030440947"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-44094-7_15","type":"book-chapter","created":{"date-parts":[[2020,4,8]],"date-time":"2020-04-08T23:13:57Z","timestamp":1586387637000},"page":"228-243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Detection of Frailty Using Genetic Programming"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4005-1238","authenticated-orcid":false,"given":"Adane","family":"Tarekegn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8749-9737","authenticated-orcid":false,"given":"Fulvio","family":"Ricceri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe","family":"Costa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elisa","family":"Ferracin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7647-5649","authenticated-orcid":false,"given":"Mario","family":"Giacobini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,9]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","first-page":"23","DOI":"10.2147\/RMHP.S168750","volume":"12","author":"G Kojima","year":"2019","unstructured":"Kojima, G., Liljas, A., Iliffe, S.: Frailty syndrome: implications and challenges for health care policy. Risk Manag. Healthc. Policy 12, 23\u201330 (2019). https:\/\/doi.org\/10.2147\/RMHP.S168750","journal-title":"Risk Manag. Healthc. Policy"},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1093\/ageing\/afv196","volume":"45","author":"TA Comans","year":"2016","unstructured":"Comans, T.A., Peel, N.M., Hubbard, R.E., Mulligan, A.D., Gray, L.C., Scuffham, P.A.: The increase in healthcare costs associated with frailty in older people discharged to a post-acute transition care program. Age Ageing 45, 317\u2013320 (2016). https:\/\/doi.org\/10.1093\/ageing\/afv196","journal-title":"Age Ageing"},{"key":"15_CR3","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1016\/S0140-6736(12)62167-9","volume":"381","author":"A Clegg","year":"2013","unstructured":"Clegg, A., Young, J., Iliffe, S., Rikkert, M.O., Rockwood, K.: Frailty in elderly people. Lancet 381, 752\u2013762 (2013). https:\/\/doi.org\/10.1016\/S0140-6736(12)62167-9","journal-title":"Lancet"},{"key":"15_CR4","unstructured":"Wennberg, D., Siegel, M., Darin, B., Filipova, N.: Combined predictive model: final report and technical documentation (2006)"},{"key":"15_CR5","doi-asserted-by":"publisher","unstructured":"Lally, F., Crome, P.: Understanding frailty (2007). https:\/\/doi.org\/10.1136\/pgmj.2006.048587","DOI":"10.1136\/pgmj.2006.048587"},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"Fried, L.P., et al.: Frailty in older adults: evidence for a phenotype. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 56, M146\u2013M157 (2001). https:\/\/doi.org\/10.1093\/gerona\/56.3.M146","DOI":"10.1093\/gerona\/56.3.M146"},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1503\/cmaj.050051","volume":"173","author":"K Rockwood","year":"2005","unstructured":"Rockwood, K., et al.: A global clinical measure of fitness and frailty in elderly people. CMAJ 173, 489\u2013495 (2005). https:\/\/doi.org\/10.1503\/cmaj.050051","journal-title":"CMAJ"},{"key":"15_CR8","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s10462-007-9052-3","volume":"26","author":"SB Kotsiantis","year":"2006","unstructured":"Kotsiantis, S.B., et al.: Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26, 159\u2013190 (2006). https:\/\/doi.org\/10.1007\/s10462-007-9052-3","journal-title":"Artif. Intell. Rev."},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1093\/gerona\/62.7.738","volume":"62","author":"K Rockwood","year":"2007","unstructured":"Rockwood, K., Andrew, M., Mitnitski, A.: A comparison of two approaches to measuring frailty in elderly people. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 62, 738\u2013743 (2007). https:\/\/doi.org\/10.1093\/gerona\/62.7.738","journal-title":"J. Gerontol. Ser. A Biol. Sci. Med. Sci."},{"key":"15_CR10","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.archger.2015.01.016","volume":"60","author":"J Blodgett","year":"2015","unstructured":"Blodgett, J., Theou, O., Kirkland, S., Andreou, P., Rockwood, K.: Frailty in NHANES: comparing the frailty index and phenotype. Arch. Gerontol. Geriatr. 60, 464\u2013470 (2015). https:\/\/doi.org\/10.1016\/j.archger.2015.01.016","journal-title":"Arch. Gerontol. Geriatr."},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.1111\/jgs.12420","volume":"61","author":"O Theou","year":"2013","unstructured":"Theou, O., Brothers, T.D., Mitnitski, A., Rockwood, K.: Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality. J. Am. Geriatr. Soc. 61, 1537\u20131551 (2013). https:\/\/doi.org\/10.1111\/jgs.12420","journal-title":"J. Am. Geriatr. Soc."},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Katz, A., Wong, S., Williamson, T., Taylor, C., Peterson, S.: Identification of frailty using EMR and admin data: a complex issue. Int. J. Popul. Data Sci. 3 (2018). https:\/\/doi.org\/10.23889\/ijpds.v3i4.832","DOI":"10.23889\/ijpds.v3i4.832"},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"S43","DOI":"10.1016\/s0167-4943(10)70012-1","volume":"50","author":"C-Y Chen","year":"2010","unstructured":"Chen, C.-Y., Wu, S.-C., Chen, L.-J., Lue, B.-H.: The prevalence of subjective frailty and factors associated with frailty in Taiwan. Arch. Gerontol. Geriatr. 50, S43\u2013S47 (2010). https:\/\/doi.org\/10.1016\/s0167-4943(10)70012-1","journal-title":"Arch. Gerontol. Geriatr."},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1161\/CIRCULATIONAHA.108.841437","volume":"121","author":"DH Lee","year":"2010","unstructured":"Lee, D.H., Buth, K.J., Martin, B.J., Yip, A.M., Hirsch, G.M.: Frail patients are at increased risk for mortality and prolonged institutional care after cardiac surgery. Circulation 121, 973 (2010). https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.108.841437","journal-title":"Circulation"},{"key":"15_CR15","doi-asserted-by":"publisher","first-page":"744.e17","DOI":"10.1016\/j.amjmed.2017.01.003","volume":"130","author":"ML Homer","year":"2017","unstructured":"Homer, M.L., Palmer, N.P., Fox, K.P., Armstrong, J., Mandl, K.D.: Predicting falls in people aged 65 years and older from insurance claims. Am. J. Med. 130, 744.e17\u2013744.e23 (2017). https:\/\/doi.org\/10.1016\/j.amjmed.2017.01.003","journal-title":"Am. J. Med."},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1109\/JPROC.2018.2791463","volume":"106","author":"F Bertini","year":"2018","unstructured":"Bertini, F., Bergami, G., Montesi, D., Veronese, G., Marchesini, G., Pandolfi, P.: Predicting frailty condition in elderly using multidimensional socioclinical databases. Proc. IEEE 106, 723\u2013737 (2018). https:\/\/doi.org\/10.1109\/JPROC.2018.2791463","journal-title":"Proc. IEEE"},{"key":"15_CR17","series-title":"Applied Mathematical Sciences","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-4-431-55978-8_11","volume-title":"Information Geometry and Its Applications","author":"S Amari","year":"2016","unstructured":"Amari, S.: Machine learning. In: Amari, S. (ed.) Information Geometry and Its Applications. AMS, vol. 194, pp. 231\u2013278. Springer, Tokyo (2016). https:\/\/doi.org\/10.1007\/978-4-431-55978-8_11"},{"key":"15_CR18","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/ida-2002-6504","volume":"6","author":"N Japkowicz","year":"2018","unstructured":"Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6, 429\u2013449 (2018). https:\/\/doi.org\/10.3233\/ida-2002-6504","journal-title":"Intell. Data Anal."},{"key":"15_CR19","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/S0031-3203(02)00257-1","volume":"36","author":"R Barandela","year":"2003","unstructured":"Barandela, R., S\u00e1nchez, J.S., Garc\u00eda, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recogn. 36, 849\u2013851 (2003). https:\/\/doi.org\/10.1016\/S0031-3203(02)00257-1","journal-title":"Pattern Recogn."},{"key":"15_CR20","doi-asserted-by":"publisher","unstructured":"McCarthy, K., Zabar, B., Weiss, G.: Does cost-sensitive learning beat sampling for classifying rare classes? In: Proceedings of the 1st International Workshop on Utility-based Data Mining - UBDM 2005, pp. 69\u201377. ACM Press, New York (2005). https:\/\/doi.org\/10.1145\/1089827.1089836","DOI":"10.1145\/1089827.1089836"},{"key":"15_CR21","unstructured":"Chen, J.X., Cheng, T.H., Chan, A.L.F., Wang, H.Y.: An application of classification analysis for skewed class distribution in therapeutic drug monitoring - the case of vancomycin. In: Proceedings - IDEAS Workshop on Medical Information Systems: The Digital Hospital, IDEAS 2004-DH (2005)"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Orriols, A., Bernad\u00ed-Mansilla, E.: Class imbalance problem in UCS classifier system: fitness adaptation. In: 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005, Proceedings (2005)","DOI":"10.1145\/1102256.1102271"},{"key":"15_CR23","doi-asserted-by":"publisher","unstructured":"Azimlu, F., Rahnamayan, S., Makrehchi, M., Kalra, N.: Comparing genetic programming with other data mining techniques on prediction models. In: 2019 14th International Conference on Computer Science & Education (ICCSE), pp. 785\u2013791. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCSE.2019.8845381","DOI":"10.1109\/ICCSE.2019.8845381"},{"key":"15_CR24","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1186\/1471-2105-14-55","volume":"14","author":"S Amal","year":"2013","unstructured":"Amal, S., Periwal, V., Scaria, V.: Predictive modeling of anti-malarial molecules inhibiting Apicoplast formation. BMC Bioinf. 14, 55 (2013). https:\/\/doi.org\/10.1186\/1471-2105-14-55","journal-title":"BMC Bioinf."},{"key":"15_CR25","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., Bing, G.: Learning from class-imbalanced data: review of methods and applications. Expert Syst. Appl. 73, 220\u2013239 (2017). https:\/\/doi.org\/10.1016\/j.eswa.2016.12.035","journal-title":"Expert Syst. Appl."},{"key":"15_CR26","doi-asserted-by":"publisher","first-page":"4263","DOI":"10.1109\/TCYB.2016.2606104","volume":"47","author":"Q Kang","year":"2017","unstructured":"Kang, Q., Chen, X.S., Li, S.S., Zhou, M.C.: A noise-filtered under-sampling scheme for imbalanced classification. IEEE Trans. Cybern. 47, 4263\u20134274 (2017). https:\/\/doi.org\/10.1109\/TCYB.2016.2606104","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"15_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-018-0151-6","volume":"5","author":"JL Leevy","year":"2018","unstructured":"Leevy, J.L., Khoshgoftaar, T.M., Bauder, R.A., Seliya, N.: A survey on addressing high-class imbalance in big data. J. Big Data 5(1), 1\u201330 (2018). https:\/\/doi.org\/10.1186\/s40537-018-0151-6","journal-title":"J. Big Data"},{"key":"15_CR28","doi-asserted-by":"publisher","unstructured":"Han, J., Kamber, M., Pei, J.: Data Mining. Elsevier, Amsterdam (2012). https:\/\/doi.org\/10.1016\/C2009-0-61819-5","DOI":"10.1016\/C2009-0-61819-5"},{"key":"15_CR29","unstructured":"Volrathongchai, K., Brennan, P.F., Ferris, M.C.: Predicting the likelihood of falls among the elderly using likelihood basis pursuit technique. In: AMIA Annual Symposium, Proceedings (2005)"},{"key":"15_CR30","doi-asserted-by":"publisher","unstructured":"Bannister, C.A., Halcox, J.P., Currie, C.J., Preece, A., Spasi\u0107, I.: A genetic programming approach to development of clinical prediction models: a case study in symptomatic cardiovascular disease. PLoS One (2018). https:\/\/doi.org\/10.1371\/journal.pone.0202685","DOI":"10.1371\/journal.pone.0202685"},{"key":"15_CR31","doi-asserted-by":"publisher","first-page":"A200","DOI":"10.1016\/j.jval.2014.03.1171","volume":"17","author":"CA Bannister","year":"2014","unstructured":"Bannister, C.A., Currie, C.J., Preece, A., Spasic, I.: Automatic development of clinical prediction models with genetic programming: a case study in cardiovascular disease. Value Health 17, A200\u2013A201 (2014). https:\/\/doi.org\/10.1016\/j.jval.2014.03.1171","journal-title":"Value Health"},{"key":"15_CR32","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-1-4614-6940-7_6","volume-title":"Search Methodologies","author":"R Poli","year":"2014","unstructured":"Poli, R., Koza, J.: Genetic programming. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 143\u2013185. Springer, Boston (2014). https:\/\/doi.org\/10.1007\/978-1-4614-6940-7_6"},{"key":"15_CR33","unstructured":"HeuristicLab homepage. https:\/\/dev.heuristiclab.com\/trac.fcgi\/wiki"},{"key":"15_CR34","doi-asserted-by":"publisher","unstructured":"Vluymans, S.: Learning from imbalanced data. In: Studies in Computational Intelligence, pp. 81\u2013110 (2019). https:\/\/doi.org\/10.1007\/978-3-030-04663-7_4","DOI":"10.1007\/978-3-030-04663-7_4"},{"key":"15_CR35","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1080\/08839514.2018.1508839","volume":"32","author":"RL Ulloa-Cazarez","year":"2018","unstructured":"Ulloa-Cazarez, R.L., L\u00f3pez-Mart\u00edn, C., Abran, A., Y\u00e1\u00f1ez-M\u00e1rquez, C.: Prediction of online students performance by means of genetic programming. Appl. Artif. Intell. 32, 858\u2013881 (2018). https:\/\/doi.org\/10.1080\/08839514.2018.1508839","journal-title":"Appl. Artif. Intell."},{"key":"15_CR36","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.cor.2011.05.004","volume":"39","author":"B Can","year":"2012","unstructured":"Can, B., Heavey, C.: A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Comput. Oper. Res. 39, 424\u2013436 (2012). https:\/\/doi.org\/10.1016\/j.cor.2011.05.004","journal-title":"Comput. Oper. Res."}],"container-title":["Lecture Notes in Computer Science","Genetic Programming"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-44094-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T18:11:27Z","timestamp":1614881487000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-44094-7_15"}},"subtitle":["The Case of Older People in Piedmont, Italy"],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030440930","9783030440947"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-44094-7_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"9 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EuroGP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Genetic Programming (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seville","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eurogp2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.evostar.org\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}