A machine learning framework for assessing the risk of venous thromboembolism in patients undergoing hip or knee replacement

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dc.authoridDursun Delen / 0000-0001-8857-5148en_US
dc.authorscopusidDursun Delen / 55887961100
dc.authorwosidDursun Delen / AGA-9892-2022en_US
dc.contributor.authorDezfouli, Elham Rasouli
dc.contributor.authorDelen, Dursun
dc.contributor.authorZhao, Huimin
dc.contributor.authorDavazdahemami, Behrooz
dc.date.accessioned2022-11-08T12:47:02Z
dc.date.available2022-11-08T12:47:02Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractVenous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patientsen_US
dc.identifier.citationRasouli Dezfouli, E., Delen, D., Zhao, H. et al. A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement. J Healthc Inform Res (2022). https://doi.org/10.1007/s41666-022-00121-2en_US
dc.identifier.doi10.1007/s41666-022-00121-2en_US
dc.identifier.issn2509-4971en_US
dc.identifier.issn2509-498Xen_US
dc.identifier.scopus2-s2.0-85140650028en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1007/s41666-022-00121-2
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3252
dc.identifier.wosWOS:000871801500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDelen, Dursun
dc.language.isoenen_US
dc.publisherSPRINGERNATUREen_US
dc.relation.ispartofJOURNAL OF HEALTHCARE INFORMATICS RESEARCHen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectVTEen_US
dc.subjectPredictionen_US
dc.subjectRisken_US
dc.subjectDVTen_US
dc.subjectDeep Learningen_US
dc.subjectGenetic Algorithmen_US
dc.titleA machine learning framework for assessing the risk of venous thromboembolism in patients undergoing hip or knee replacementen_US
dc.typeArticleen_US

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