Streamlining patients' opioid prescription dosage: an explanatory bayesian model

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dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.authoridAydas, Osman/0000-0002-3353-8048
dc.authorwosidDelen, Dursun/AGA-9892-2022
dc.contributor.authorAsilkalkan, Abdullah
dc.contributor.authorDag, Asli Z.
dc.contributor.authorSimsek, Serhat
dc.contributor.authorAydas, Osman T.
dc.contributor.authorKibis, Eyyub Y.
dc.contributor.authorDelen, Dursun
dc.date.accessioned2024-05-19T14:42:47Z
dc.date.available2024-05-19T14:42:47Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractNearly half a million people died between 1999 and 2019 from overdosing on both prescribed and illicit opioids. Thus, much research has been devoted to determining the factors affecting the dosages of opioid prescriptions. In this study, we build a probabilistic data-driven framework that develops Tree Augmented Naive Bayes (TAN) models to predict patients' opioid prescription dosage categories and investigate the conditional interrelations among these predictors. As this framework is rooted in the CDC's prescription guidelines, it can be applied in clinical settings by focusing primarily on pre-discharge pain assessments. Following data acquisition and cleaning, we utilize Elastic Net (EN) and Genetic Algorithm (GA) to identify the most important predictors. Next, Synthetic Minority Oversampling Technique (SMOTE), and Random Under Sampling (RUS) are employed to overcome the data imbalance problem present in the dataset. A patient's gender, income level, smoking status, BMI, age, and length of stay at the hospital are identified as the most significant predictors for opioid prescription dosage. In addition, we construct a Bayesian Belief Network (BBN) model, which reveals that the effect of smoking status and gender in predicting opioid prescription dosage depends on the patient's income level. Finally, a web-based decision support tool that can help surgeons better assess and prescribe appropriate opioid dosages for patients is built.en_US
dc.identifier.doi10.1007/s10479-023-05709-4
dc.identifier.issn0254-5330
dc.identifier.issn1572-9338
dc.identifier.scopus2-s2.0-85176781966en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s10479-023-05709-4
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5284
dc.identifier.wosWOS:001103398200004en_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/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectInterpretable Aien_US
dc.subjectLimeen_US
dc.subjectOpioid Prescriptionen_US
dc.subjectTree Augmented Naive Bayesen_US
dc.titleStreamlining patients' opioid prescription dosage: an explanatory bayesian modelen_US
dc.typeArticleen_US

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