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dc.contributor.authorHautala, Arto J.
dc.contributor.authorShavazipour, Babooshka
dc.contributor.authorAfsar, Bekir
dc.contributor.authorTulppo, Mikko P.
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2023-09-01T10:52:25Z
dc.date.available2023-09-01T10:52:25Z
dc.date.issued2023
dc.identifier.citationHautala, A. J., Shavazipour, B., Afsar, B., Tulppo, M. P., & Miettinen, K. (2023). Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome : A prospective pilot study. <i>Cardiovascular Digital Health Journal</i>, <i>4</i>(4), 137-142. <a href="https://doi.org/10.1016/j.cvdhj.2023.05.001" target="_blank">https://doi.org/10.1016/j.cvdhj.2023.05.001</a>
dc.identifier.otherCONVID_183230136
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/88851
dc.description.abstractBackground Health care budgets are limited requiring the optimal use of resources. Machine learning (ML) methods may have an enormous potential for effective use of health care resources. Objective We assessed the applicability of selected ML tools to evaluate the contribution of known risk markers for prognosis of coronary artery disease to predict health care costs for all reasons in patients with a recent acute coronary syndrome (n = 65, aged 65 ± 9 years) for one-year follow-up. Methods Risk markers were assessed at baseline, and health care costs were collected from electronic health registries. The Cross-decomposition algorithms were used to rank the considered risk markers based on their impacts on variances. Then regression analysis was performed to predict costs by entering the first top-ranking risk marker and adding the next best markers, one-by-one, to built-up altogether 13 predictive models. Results The average annual health care costs were €2601±5378 per patient. The Depression Scale showed the highest predictive value (r = 0.395), accounting for 16% of the costs (p=0.001). When the next two ranked markers (LDL cholesterol; r = 0.230 and left ventricular ejection fraction; r= - 0.227, respectively) were added to the model, the predictive value was 24 % for the costs (p=0.001). Conclusion Higher depression score is the primary variable forecasting health care costs in one-year follow-up among acute coronary syndrome patients. The ML tools may help decision-making when planning optimal utilization of treatment strategies.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesCardiovascular Digital Health Journal
dc.rightsCC BY 4.0
dc.subject.othercoronary artery disease
dc.subject.othercoronary heart disease
dc.subject.otherartificial intelligence
dc.subject.otherhealth care costs
dc.subject.othereconomic evaluation
dc.titleMachine learning models in predicting health care costs in patients with a recent acute coronary syndrome : A prospective pilot study
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202309014885
dc.contributor.laitosLiikuntatieteellinen tiedekuntafi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Sport and Health Sciencesen
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineHyvinvoinnin tutkimuksen yhteisöfi
dc.contributor.oppiaineMultiobjective Optimization Groupfi
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineSchool of Wellbeingen
dc.contributor.oppiaineMultiobjective Optimization Groupen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange137-142
dc.relation.issn2666-6936
dc.relation.numberinseries4
dc.relation.volume4
dc.type.versionpublishedVersion
dc.rights.copyright© 2023 Heart Rhythm Society
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber322221
dc.subject.ysosydäntaudit
dc.subject.ysotaloudellinen arviointi
dc.subject.ysoresurssit
dc.subject.ysokoneoppiminen
dc.subject.ysokustannukset
dc.subject.ysoennusteet
dc.subject.ysoterveydenhoito
dc.subject.ysotekoäly
dc.subject.ysosepelvaltimotauti
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p2710
jyx.subject.urihttp://www.yso.fi/onto/yso/p11121
jyx.subject.urihttp://www.yso.fi/onto/yso/p19352
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p7517
jyx.subject.urihttp://www.yso.fi/onto/yso/p3297
jyx.subject.urihttp://www.yso.fi/onto/yso/p2641
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p16060
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.cvdhj.2023.05.001
dc.relation.funderSuomen Akatemiafi
dc.relation.funderResearch Council of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationThis study was partly funded by the Academy of Finland (grant no. 322221).
dc.type.okmA1


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