Repository logo
 
Publication

Predicting Function Delay with a Machine Learning Model: Improve the Long-term Survival of Pancreatic Grafts

2022Journal article Open access
Simple item page
dc.contributor.authorVigia, E
dc.contributor.authorRamalhete, L
dc.contributor.authorBarros, I
dc.contributor.authorChumbinho, B
dc.contributor.authorFilipe, E
dc.contributor.authorPena, A
dc.contributor.authorBicho, L
dc.contributor.authorNobre, A
dc.contributor.authorCarrelha, S
dc.contributor.authorCorado, S
dc.contributor.authorSobral, M
dc.contributor.authorLamelas, J
dc.contributor.authorSantos Coelho, J
dc.contributor.authorPinto Marques, H
dc.contributor.authorPico, P
dc.contributor.authorCosta, S
dc.contributor.authorRodrigues, F
dc.contributor.authorBigotte Vieira, M
dc.contributor.authorMagriço, R
dc.contributor.authorCotovio, P
dc.contributor.authorCaeiro, F
dc.contributor.authorAires, I
dc.contributor.authorSilva, C
dc.contributor.authorRemédio, F
dc.contributor.authorMartins, A
dc.contributor.authorFerreira, A
dc.contributor.authorPaulino, J
dc.contributor.authorNolasco, F
dc.contributor.authorRibeiro, R
dc.date.accessioned2023-08-10T11:36:48Z
dc.date.available2023-08-10T11:36:48Z
dc.date.issued2022
dc.description.abstractThe impact of delayed graft function on outcomes following various solid organ transplants is well documented and addressed in the literature. Delayed graft function following various solid organ transplants is associated with both short- and long-term graft survival issues. In a retrospective cohort study including 106 patients we evaluated whether pancreas graft survival differs according to moment of insulin therapy following simultaneous pancreaskidney transplant. As a result, we aimed to identify possible risk factors and build a machine-learning-based model that predicts the likelihood of dysfunction following SPK transplant patients based on day zero data after transplant, allowing to enhance pancreatic graft survival. Feature selection by Relief algorithm yielded donor features, age, cause of death, hemoglobin, gender, ventilation days, days in ICU, length of cardiac respiratory arrest and recipient features, gender, long-term insulin, dialysis type, time of diabetes mellitus, vPRA pre-Tx, number of HLA-A mismatches and PRDI, all contributed to the models' strength.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPancreat Disord Ther.2022; 12(3):1000231pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.17/4632
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherLongdom Publishing SLpt_PT
dc.subjectPancreas Transplantationpt_PT
dc.subjectGraft Rejectionpt_PT
dc.subjectGraft Survivalpt_PT
dc.subjectHCC CHBPTpt_PT
dc.titlePredicting Function Delay with a Machine Learning Model: Improve the Long-term Survival of Pancreatic Graftspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue3pt_PT
oaire.citation.startPage1000231pt_PT
oaire.citation.volume12pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Pancreat Disord Ther 2022_12.pdf
Size:
681.42 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

CHBPT - Artigos