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Evolution of the Data Mining and Machine Learning Techniques Used in Health Care: A Scoping Review

dc.contributor.authorSanchez Zuleta C.C
dc.contributor.authorGiraldo Marín L.M
dc.contributor.authorVélez Gómez J.F
dc.contributor.authorSanguino Cotte D
dc.contributor.authorVargas López C.A
dc.contributor.authorJaimes Barragán F.A.
dc.date.accessioned2022-09-14T14:33:44Z
dc.date.available2022-09-14T14:33:44Z
dc.date.created2021
dc.identifier.isbn9783030726508
dc.identifier.issn21945357
dc.identifier.urihttp://hdl.handle.net/11407/7454
dc.descriptionThe purpose of this scoping review was to observe the evolution of using data mining and machine learning techniques in health care based on the MEDLINE database. We used PRISMA-ScR to observe the techniques evolution and its usage according to the number of scientific publications that reference them from 2000 to 2018. On the basis of the results, we established two search strategies when performing a query about the subject. We also found that the three main techniques used in health care are “cluster,” “support vector machine,” and “neural networks.” © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.eng
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85107326029&doi=10.1007%2f978-3-030-72651-5_15&partnerID=40&md5=9bcb1f7f36288eb645bf535938d241b1
dc.sourceAdvances in Intelligent Systems and Computing
dc.titleEvolution of the Data Mining and Machine Learning Techniques Used in Health Care: A Scoping Review
dc.typeConference Paper
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programCiencias Básicas
dc.publisher.programIngeniería de Sistemas
dc.type.spaDocumento de conferencia
dc.identifier.doi10.1007/978-3-030-72651-5_15
dc.subject.keywordDatabase researcheng
dc.subject.keywordHealth careeng
dc.subject.keywordMachine learningeng
dc.subject.keywordMeSHeng
dc.subject.keywordScoping revieweng
dc.subject.keywordHealth careeng
dc.subject.keywordInformation systemseng
dc.subject.keywordInformation useeng
dc.subject.keywordLearning systemseng
dc.subject.keywordQuery processingeng
dc.subject.keywordSupport vector machineseng
dc.subject.keywordMachine learning techniqueseng
dc.subject.keywordMEDLINE databaseeng
dc.subject.keywordScientific publicationseng
dc.subject.keywordScoping revieweng
dc.subject.keywordSearch strategieseng
dc.subject.keywordData miningeng
dc.relation.citationvolume1366 AISC
dc.relation.citationstartpage151
dc.relation.citationendpage160
dc.publisher.facultyFacultad de Ingenierías
dc.publisher.facultyFacultad de Ciencias Básicas
dc.affiliationSanchez Zuleta, C.C., Faculty of Basic Sciences, Universidad de Medellín, Medellín, Colombia
dc.affiliationGiraldo Marín, L.M., Faculty of Engineering, Universidad de Medellín, Medellín, Colombia
dc.affiliationVélez Gómez, J.F., Faculty of Basic Sciences, Universidad de Medellín, Medellín, Colombia
dc.affiliationSanguino Cotte, D., Hospital Universitario San Vicente Fundación, Medellín, Colombia
dc.affiliationVargas López, C.A., Hospital Universitario San Vicente Fundación, Medellín, Colombia
dc.affiliationJaimes Barragán, F.A., Hospital Universitario San Vicente Fundación, Medellín, Colombia
dc.relation.referencesAnderson, S., Allen, P., Peckham, S., Goodwin, N., Asking the right questions: Scoping studies in the commissioning of research on the organisation and delivery of health services (2008) Health Res Policy Syst, 6 (7), pp. 1-12
dc.relation.referencesStorey, V., Song, I.-Y.: Big data technologies and management: what conceptual modeling can do. In: Data & Knowledge Engineering. Science Direct, pp. 50–67 (2017). https://www.sci encedirect.com/science/article/abs/pii/S0169023X17300277?via%3Dihub. Accessed 06 May 2020
dc.relation.referencesMcCrae, I., Hempstalk, K.: Introduction to machine learning in healthcare. In: ORION HEALTH. 2015. https://orionhealth.com/uk/knowledge-hub/reports/machine-learning-in-hea lthcare/. Accessed 06 May 2020
dc.relation.referencesShalev-Shwartz, S., Ben-David, S.: Understanding machine learning: from theory to algorithms. In: Cambridge University Press. 2014. https://www.cs.huji.ac.il/~shais/Understandin gMachineLearning/understanding-machine-learning-theory-algorithms.pdf. Accessed 06 May 2020
dc.relation.referencesIONIŢĂ
dc.relation.references, I., IONIŢĂ, L.: Applying data mining techniques in healthcare. Stud. Inform. Control. 25(3), 385–394. 2016. https://sic.ici.ro/wp-content/uploads/2016/09/SIC-3-2016-Art12. pdf. Accessed 06 May 2020
dc.relation.referencesTricco, A.C., Lillie, E., Zarin, W., O’Brien, K.K., Colquhoun, H., Levac, D., Weeks, L., PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation (2018) Ann. Intern. Med., 169 (7), pp. 467-473
dc.relation.references(2019) Library of the Medicine. MEDLINE®: Description of the Database. In: National Library of the Medicine, , https://www.nlm.nih.gov/bsd/medline.html. Accessed
dc.relation.referencesSevilla, B.U.: MeSH Database. In: Biblioteca Universidad de Sevilla. 2019. http://fama2.us. es/bgu/ad/tfg/pubmed/PubMed_08.htm. Accessed 06 May 2020
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/other
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín
dc.relation.ispartofconferenceWorld Conference on Information Systems and Technologies, WorldCIST 2021


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