Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.
| Title: | Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients. |
|---|---|
| Authors: | Andishgar, Aref, Bazmi, Sina, Lankarani, Kamran B., Taghavi, Seyed Alireza, Imanieh, Mohammad Hadi, Sivandzadeh, Gholamreza, Saeian, Samira, Dadashpour, Nazanin, Shamsaeefar, Alireza, Ravankhah, Mahdi, Deylami, Hamed Nikoupour, Tabrizi, Reza, Imanieh, Mohammad Hossein |
| Source: | Scientific Reports; 2/8/2025, Vol. 15 Issue 1, p1-14, 14p |
| Subject Terms: | MACHINE learning, FEATURE selection, MORTALITY risk factors, SURVIVAL rate, SURVIVAL analysis (Biometry) |
| Abstract: | Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with a median 19-month follow-up. BC was diagnosed with ultrasounds and MRCP. Missing data was imputed using mean and median. Data preprocessing involved feature scaling and one-hot encoding. Survival analysis used filter (Cox-P, Cox-c) and embedded (RSF, LASSO) feature selection methods. Seven survival machine learning algorithms were used: LASSO, Ridge, RSF, E-NET, GBS, C-GBS, and FS-SVM. Model development employed 5-fold cross-validation, random oversampling, and hyperparameter tuning. Random oversampling addressed data imbalance. Optimal hyperparameters were determined based on average C-index. Features importance was assessed using standardized regression coefficients and permutation importance for top models. Stability was evaluated using 5-fold cross-validation standard deviation. Finally, 1799 observations with 40 outcome predictors were included. RSF with Ridge achieved the highest performance (C-index: 0.699) for BC prediction, while RSF with RSF had the highest performance (C-index: 0.784) for mortality prediction. Top BC predictors were LT graft types, IBD in recipients, recipient's BMI, recipient's history of PVT, and previous LT history. For mortality, they were post-transplant AST, creatinine, recipient's age, post-transplant ALT, and tacrolimus consumption. We identified BC and mortality risk factors, improving decision-making and outcomes. [ABSTRACT FROM AUTHOR] |
| Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| Full text is not displayed to guests. | Login for full access. |
| FullText | Links: – Type: pdflink Text: Availability: 1 CustomLinks: – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edb&genre=article&issn=20452322&ISBN=&volume=15&issue=1&date=20250208&spage=1&pages=1-14&title=Scientific Reports&atitle=Comparison%20of%20time-to-event%20machine%20learning%20models%20in%20predicting%20biliary%20complication%20and%20mortality%20rate%20in%20liver%20transplant%20patients.&aulast=Andishgar%2C%20Aref&id=DOI:10.1038/s41598-025-89570-4 Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 182883260 RelevancyScore: 1060 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.46936035156 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Andishgar%2C+Aref%22">Andishgar, Aref</searchLink><br /><searchLink fieldCode="AR" term="%22Bazmi%2C+Sina%22">Bazmi, Sina</searchLink><br /><searchLink fieldCode="AR" term="%22Lankarani%2C+Kamran+B%2E%22">Lankarani, Kamran B.</searchLink><br /><searchLink fieldCode="AR" term="%22Taghavi%2C+Seyed+Alireza%22">Taghavi, Seyed Alireza</searchLink><br /><searchLink fieldCode="AR" term="%22Imanieh%2C+Mohammad+Hadi%22">Imanieh, Mohammad Hadi</searchLink><br /><searchLink fieldCode="AR" term="%22Sivandzadeh%2C+Gholamreza%22">Sivandzadeh, Gholamreza</searchLink><br /><searchLink fieldCode="AR" term="%22Saeian%2C+Samira%22">Saeian, Samira</searchLink><br /><searchLink fieldCode="AR" term="%22Dadashpour%2C+Nazanin%22">Dadashpour, Nazanin</searchLink><br /><searchLink fieldCode="AR" term="%22Shamsaeefar%2C+Alireza%22">Shamsaeefar, Alireza</searchLink><br /><searchLink fieldCode="AR" term="%22Ravankhah%2C+Mahdi%22">Ravankhah, Mahdi</searchLink><br /><searchLink fieldCode="AR" term="%22Deylami%2C+Hamed+Nikoupour%22">Deylami, Hamed Nikoupour</searchLink><br /><searchLink fieldCode="AR" term="%22Tabrizi%2C+Reza%22">Tabrizi, Reza</searchLink><br /><searchLink fieldCode="AR" term="%22Imanieh%2C+Mohammad+Hossein%22">Imanieh, Mohammad Hossein</searchLink> – Name: TitleSource Label: Source Group: Src Data: Scientific Reports; 2/8/2025, Vol. 15 Issue 1, p1-14, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22FEATURE+selection%22">FEATURE selection</searchLink><br /><searchLink fieldCode="DE" term="%22MORTALITY+risk+factors%22">MORTALITY risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22SURVIVAL+rate%22">SURVIVAL rate</searchLink><br /><searchLink fieldCode="DE" term="%22SURVIVAL+analysis+%28Biometry%29%22">SURVIVAL analysis (Biometry)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with a median 19-month follow-up. BC was diagnosed with ultrasounds and MRCP. Missing data was imputed using mean and median. Data preprocessing involved feature scaling and one-hot encoding. Survival analysis used filter (Cox-P, Cox-c) and embedded (RSF, LASSO) feature selection methods. Seven survival machine learning algorithms were used: LASSO, Ridge, RSF, E-NET, GBS, C-GBS, and FS-SVM. Model development employed 5-fold cross-validation, random oversampling, and hyperparameter tuning. Random oversampling addressed data imbalance. Optimal hyperparameters were determined based on average C-index. Features importance was assessed using standardized regression coefficients and permutation importance for top models. Stability was evaluated using 5-fold cross-validation standard deviation. Finally, 1799 observations with 40 outcome predictors were included. RSF with Ridge achieved the highest performance (C-index: 0.699) for BC prediction, while RSF with RSF had the highest performance (C-index: 0.784) for mortality prediction. Top BC predictors were LT graft types, IBD in recipients, recipient's BMI, recipient's history of PVT, and previous LT history. For mortality, they were post-transplant AST, creatinine, recipient's age, post-transplant ALT, and tacrolimus consumption. We identified BC and mortality risk factors, improving decision-making and outcomes. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edb&AN=182883260 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41598-025-89570-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: MACHINE learning Type: general – SubjectFull: FEATURE selection Type: general – SubjectFull: MORTALITY risk factors Type: general – SubjectFull: SURVIVAL rate Type: general – SubjectFull: SURVIVAL analysis (Biometry) Type: general Titles: – TitleFull: Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Andishgar, Aref – PersonEntity: Name: NameFull: Bazmi, Sina – PersonEntity: Name: NameFull: Lankarani, Kamran B. – PersonEntity: Name: NameFull: Taghavi, Seyed Alireza – PersonEntity: Name: NameFull: Imanieh, Mohammad Hadi – PersonEntity: Name: NameFull: Sivandzadeh, Gholamreza – PersonEntity: Name: NameFull: Saeian, Samira – PersonEntity: Name: NameFull: Dadashpour, Nazanin – PersonEntity: Name: NameFull: Shamsaeefar, Alireza – PersonEntity: Name: NameFull: Ravankhah, Mahdi – PersonEntity: Name: NameFull: Deylami, Hamed Nikoupour – PersonEntity: Name: NameFull: Tabrizi, Reza – PersonEntity: Name: NameFull: Imanieh, Mohammad Hossein IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 02 Text: 2/8/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20452322 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: Scientific Reports Type: main |
| ResultId | 1 |