Artificial neural network is highly predictive of outcome in paediatric acute liver failure
dc.contributor.author | Rajanayagam, J. | |
dc.contributor.author | Frank, Eibe | |
dc.contributor.author | Shepherd, R. W. | |
dc.contributor.author | Lewindon, P. J. | |
dc.coverage.spatial | Denmark | en_NZ |
dc.date.accessioned | 2013-07-05T02:04:43Z | |
dc.date.available | 2013-07-05T02:04:43Z | |
dc.date.copyright | 2013-06-27 | |
dc.date.issued | 2013 | |
dc.description.abstract | Current 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.identifier.citation | Rajanayagam, 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.doi | 10.1111/petr.12100 | en_NZ |
dc.identifier.uri | https://hdl.handle.net/10289/7752 | |
dc.language.iso | en | en_NZ |
dc.publisher | Wiley | en_NZ |
dc.relation.isPartOf | Pediatric Transplantation | en_NZ |
dc.relation.ispartof | Pediatric Transplantation | |
dc.relation.uri | http://onlinelibrary.wiley.com/doi/10.1111/petr.12100/abstract | en_NZ |
dc.subject | acute liver failure | en_NZ |
dc.subject | pediatric liver transplantation | en_NZ |
dc.subject | artificial neural network | en_NZ |
dc.subject | pediatric end-stage liver disease score | en_NZ |
dc.subject | Machine learning | |
dc.title | Artificial neural network is highly predictive of outcome in paediatric acute liver failure | en_NZ |
dc.type | Journal Article | en_NZ |
pubs.begin-page | 535 | en_NZ |
pubs.edition | June | en_NZ |
pubs.elements-id | 38727 | |
pubs.end-page | 542 | en_NZ |
pubs.issue | 6 | en_NZ |
pubs.volume | 17 | en_NZ |
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