A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases

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dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.authoridTopuz, Kazim/0000-0001-7990-5475
dc.authoridDavazdahemami, Behrooz/0000-0003-2885-6014
dc.authorwosidDelen, Dursun/AGA-9892-2022
dc.authorwosidTopuz, Kazim/K-8287-2014
dc.contributor.authorTopuz, Kazim
dc.contributor.authorDavazdahemami, Behrooz
dc.contributor.authorDelen, Dursun
dc.date.accessioned2024-05-19T14:46:09Z
dc.date.available2024-05-19T14:46:09Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractDuring a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure. This research integrates evolutionary search algorithms, Bayesian belief networks, and innovative interpretation techniques to provide a comprehensive exploratory-descriptive-explanatory machine learning methodology to assist clinical decision-makers in responding promptly to pandemic scenarios. The proposed approach is illustrated through a case study in which the survival of COVID-19 patients is determined using inpatient and emergency department (ED) encounters from a real-world electronic health record database. Following an exploratory phase in which genetic algorithms are used to identify a set of the most critical chronic risk factors and their validation using descriptive tools based on the concept of Bayesian Belief Nets, the framework develops and trains a probabilistic graphical model to explain and predict patient survival (with an AUC of 0.92). Finally, a publicly available online, probabilistic decision support inference simulator was constructed to facilitate what-if analysis and aid general users and healthcare professionals in interpreting model findings. The results widely corroborate intensive and expensive clinical trial research assessments.en_US
dc.identifier.doi10.1007/s10479-023-05377-4
dc.identifier.issn0254-5330
dc.identifier.issn1572-9338
dc.identifier.pmid37361089en_US
dc.identifier.scopus2-s2.0-85159726764en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s10479-023-05377-4
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5455
dc.identifier.wosWOS:000989804500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofAnnals of Operations Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectPandemicen_US
dc.subjectRisk Assessmenten_US
dc.subjectBayesian Networken_US
dc.subjectExplainable Machine Learningen_US
dc.subjectComorbidityen_US
dc.titleA Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseasesen_US
dc.typeArticleen_US

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