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Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis

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dc.contributor.author구교철-
dc.contributor.author김동욱-
dc.contributor.author박지수-
dc.contributor.author이광석-
dc.contributor.author정병하-
dc.contributor.author한웅규-
dc.date.accessioned2021-12-31T01:19:15Z-
dc.date.available2021-12-31T01:19:15Z-
dc.date.issued2021-12-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/187347-
dc.description.abstractObjectives: To develop a prediction model of spontaneous ureteral stone passage (SSP) using machine learning and logistic regression and compare the performance of the two models. Indications for management of ureteral stones are unclear, and the clinician determines whether to wait for SSP or perform active treatment, especially in well-controlled patients, to avoid unwanted complications. Therefore, suggesting the possibility of SSP would help make a clinical decision regarding ureteral stones. Methods: Patients diagnosed with unilateral ureteral stones at our emergency department between August 2014 and September 2018 were included and underwent non-contrast-enhanced computed tomography 4 weeks from the first stone episode. Predictors of SSP were applied to build and validate the prediction model using multilayer perceptron (MLP) with the Keras framework. Results: Of 833 patients, SSP was observed in 606 (72.7%). SSP rates were 68.2% and 75.6% for stone sizes 5-10 mm and <5 mm, respectively. Stone opacity, location, and whether it was the first ureteral stone episode were significant predictors of SSP. Areas under the curve (AUCs) for receiver operating characteristic (ROC) curves for MLP, and logistic regression were 0.859 and 0.847, respectively, for stones <5 mm, and 0.881 and 0.817, respectively, for 5-10 mm stones. Conclusion: SSP prediction models were developed in patients with well-controlled unilateral ureteral stones; the performance of the models was good, especially in identifying SSP for 5-10-mm ureteral stones without definite treatment guidelines. To further improve the performance of these models, future studies should focus on using machine learning techniques in image analysis.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherPublic Library of Science-
dc.relation.isPartOfPLOS ONE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDevelopment of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Urology (비뇨의학교실)-
dc.contributor.googleauthorJee Soo Park-
dc.contributor.googleauthorDong Wook Kim-
dc.contributor.googleauthorDongu Lee-
dc.contributor.googleauthorTaeju Lee-
dc.contributor.googleauthorKyo Chul Koo-
dc.contributor.googleauthorWoong Kyu Han-
dc.contributor.googleauthorByung Ha Chung-
dc.contributor.googleauthorKwang Suk Lee-
dc.identifier.doi10.1371/journal.pone.0260517-
dc.contributor.localIdA00188-
dc.contributor.localIdA05613-
dc.contributor.localIdA05336-
dc.contributor.localIdA02668-
dc.contributor.localIdA03607-
dc.contributor.localIdA04308-
dc.relation.journalcodeJ02540-
dc.identifier.eissn1932-6203-
dc.identifier.pmid34851999-
dc.contributor.alternativeNameKoo, Kyo Chul-
dc.contributor.affiliatedAuthor구교철-
dc.contributor.affiliatedAuthor김동욱-
dc.contributor.affiliatedAuthor박지수-
dc.contributor.affiliatedAuthor이광석-
dc.contributor.affiliatedAuthor정병하-
dc.contributor.affiliatedAuthor한웅규-
dc.citation.volume16-
dc.citation.number12-
dc.citation.startPagee0260517-
dc.identifier.bibliographicCitationPLOS ONE, Vol.16(12) : e0260517, 2021-12-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Urology (비뇨의학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) > 1. Journal Papers

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