Show simple item record  

dc.contributor.authorRajanayagam, J.
dc.contributor.authorFrank, Eibe
dc.contributor.authorShepherd, R. W.
dc.contributor.authorLewindon, P. J.
dc.coverage.spatialDenmarken_NZ
dc.date.accessioned2013-07-05T02:04:43Z
dc.date.available2013-07-05T02:04:43Z
dc.date.copyright2013-06-27
dc.date.issued2013
dc.identifier.citationRajanayagam, J., Frank, E., Shepherd, R. W., & Lewindon, P. J. (2013). Artificial neural network is highly predictive of outcome in paediatric acute liver failure. Pediatric Transplantation, published online 27 June 2013.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/7752
dc.description.abstractCurrent prognostic models in PALF are unreliable, failing to account for complex, non-linear relationships existing between multiple prognostic factors. A computational approach using ANN should provide superior modelling to PELD-MELD scores. We assessed the prognostic accuracy of PELD-MELD scores and ANN in PALF in children presenting to the QLTS, Australia. A comprehensive registry-based data set was evaluated in 54 children (32M, 22F, median age 17 month) with PALF. PELD-MELD scores calculated at (i) meeting PALF criteria and (ii) peak. ANN was evaluated using stratified 10-fold cross-validation. Outcomes were classified as good (transplant-free survival) or poor (death or LT) and predictive accuracy compared using AUROC curves. Mean PELD-MELD scores were significantly higher in non-transplanted non-survivors (i) 37 and (ii) 46 and transplant recipients (i) 32 and (ii) 43 compared to transplant-free survivors (i) 26 and (ii) 30. Threshold PELD-MELD scores ≥27 and ≥42, at meeting PALF criteria and peak, gave AUROC 0.71 and 0.86, respectively, for poor outcome. ANN showed superior prediction for poor outcome with AUROC 0.96, sensitivity 82.6%, specificity 96%, PPV 96.2% and NPV 85.7% (cut-off 0.5). ANN is superior to PELD-MELD for predicting poor outcome in PALF.en_NZ
dc.language.isoenen_NZ
dc.publisherWileyen_NZ
dc.relation.ispartofPediatric Transplantation
dc.relation.urihttp://onlinelibrary.wiley.com/doi/10.1111/petr.12100/abstracten_NZ
dc.subjectacute liver failureen_NZ
dc.subjectpediatric liver transplantationen_NZ
dc.subjectartificial neural networken_NZ
dc.subjectpediatric end-stage liver disease scoreen_NZ
dc.subjectMachine learning
dc.titleArtificial neural network is highly predictive of outcome in paediatric acute liver failureen_NZ
dc.typeJournal Articleen_NZ
dc.identifier.doi10.1111/petr.12100en_NZ
dc.relation.isPartOfPediatric Transplantationen_NZ
pubs.begin-page535en_NZ
pubs.editionJuneen_NZ
pubs.elements-id38727
pubs.end-page542en_NZ
pubs.issue6en_NZ
pubs.volume17en_NZ


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record