Näytä suppeat kuvailutiedot

dc.contributor.authorHautala, Arto J.
dc.contributor.authorShavazipour, Babooshka
dc.contributor.authorAfsar, Bekir
dc.contributor.authorTulppo, Mikko P.
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2024-06-04T07:12:10Z
dc.date.available2024-06-04T07:12:10Z
dc.date.issued2024
dc.identifier.citationHautala, A. J., Shavazipour, B., Afsar, B., Tulppo, M. P., & Miettinen, K. (2024). Machine learning models for assessing risk factors affecting health care costs : 12-month exercise-based cardiac rehabilitation. <i>Frontiers in Public Health</i>, <i>12</i>, Article 1378349. <a href="https://doi.org/10.3389/fpubh.2024.1378349 " target="_blank">https://doi.org/10.3389/fpubh.2024.1378349 </a>
dc.identifier.otherCONVID_216018852
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/95475
dc.description.abstractIntroduction: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals’ work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD. Methods: The data for analysis was available from a total of 71 patients referred to Oulu University Hospital, Finland, due to an acute coronary syndrome (ACS) event (75% men, age 61 ± 12 years, BMI 27 ± 4 kg/m2, ejection fraction 62 ± 8, 89% have beta-blocker medication). Risk factors were assessed at the hospital immediately after the cardiac event, and health care costs for all reasons were collected from patient registers over a year. ECR was programmed in accordance with international guidelines. Risk analysis algorithms (cross-decomposition algorithms) were employed to rank risk factors based on variances in their effects. Regression analysis was used to determine the accounting value of risk factors by entering first the risk factor with the highest degree of explanation into the model. After that, the next most potent risk factor explaining costs was added to the model one by one (13 forecast models in total). Results: The ECR group used health care services during the year at an average of 1,624 ± 2,139€ per patient. Diabetes exhibited the strongest correlation with health care expenses (r = 0.406), accounting for 16% of the total costs (p < 0.001). When the next two ranked markers (body mass index; r = 0.171 and systolic blood pressure; r = − 0.162, respectively) were added to the model, the predictive value was 18% for the costs (p = 0.004). The depression scale had the weakest independent explanation rate of all 13 risk factors (explanation value 0.1%, r = 0.029, p = 0.811). Discussion: Presence of diabetes is the primary reason forecasting health care costs in 12-month ECR intervention among ACS patients. The ML tools may help decision-making when planning the optimal allocation of health care resources.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherFrontiers Media SA
dc.relation.ispartofseriesFrontiers in Public Health
dc.rightsCC BY 4.0
dc.subject.otherexercise
dc.subject.othercardiac rehabilitation
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 for assessing risk factors affecting health care costs : 12-month exercise-based cardiac rehabilitation
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202406044238
dc.contributor.laitosLiikuntatieteellinen tiedekuntafi
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Sport and Health Sciencesen
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn2296-2565
dc.relation.volume12
dc.type.versionpublishedVersion
dc.rights.copyright© 2024 Hautala, Shavazipour, Afsar, Tulppo and Miettinen.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber322221
dc.subject.ysoharjoittelu
dc.subject.ysosepelvaltimo
dc.subject.ysotekoäly
dc.subject.ysokuntoutus
dc.subject.ysotaloudellinen arviointi
dc.subject.ysoterveydenhuoltomenot
dc.subject.ysosydän- ja verisuonitaudit
dc.subject.ysosepelvaltimotauti
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26412
jyx.subject.urihttp://www.yso.fi/onto/yso/p24430
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p3320
jyx.subject.urihttp://www.yso.fi/onto/yso/p11121
jyx.subject.urihttp://www.yso.fi/onto/yso/p9696
jyx.subject.urihttp://www.yso.fi/onto/yso/p9886
jyx.subject.urihttp://www.yso.fi/onto/yso/p16060
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3389/fpubh.2024.1378349
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationThis study was partly funded by the Academy of Finland (grant no. 322221).
dc.type.okmA1


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