ArticlePloS one2018
A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.Langenbeck's archives of surgery · 2022Pooled it
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Sarcopenia and frailty: An in-depth analysis of the pathophysiology and effect on liver transplant candidates.World journal of hepatology · 2025Review
- Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.Scientific reports · 2025Article
- Article
- A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis.PloS one · 2025Article
- Deep Learning-Based Prediction of Hepatic Decompensation in Patients With Primary Sclerosing Cholangitis With Computed Tomography.Mayo Clinic proceedings. Digital health · 2024Article
- Use of machine learning models for the prognostication of liver transplantation: A systematic review.World journal of transplantation · 2024Article
- Tutorial on survival modeling with applications to omics data.Bioinformatics (Oxford, England) · 2024Article
- A narrative review: predicting liver transplant graft survival using artificial intelligence modeling.Frontiers in transplantation · 2024Review
- Algebraic topology-based machine learning using MRI predicts outcomes in primary sclerosing cholangitis.European radiology experimental · 2022Article
- Artificial intelligence for precision medicine in autoimmune liver disease.Frontiers in immunology · 2022Review
- Personalized breast cancer onset prediction from lifestyle and health history information.PloS one · 2022Article
- Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research.World journal of hepatology · 2021Review
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2021Review
- Fully automated detection of primary sclerosing cholangitis (PSC)-compatible bile duct changes based on 3D magnetic resonance cholangiopancreatography using machine learning.European radiology · 2021Article
- Essential updates 2018/2019: Liver transplantation.Annals of gastroenterological surgery · 2020Review
- Gene expression based survival prediction for cancer patients-A topic modeling approach.PloS one · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deciding who should receive a liver transplant (LT) depends on both urgency and utility. Most survival scores are validated through discriminative tests, which compare predicted outcomes between patients. Assessing post-transplant survival utility is not discriminate, but should be "calibrated" to be effective. There are currently no such calibrated models. We developed and validated a novel calibrated model to predict individual survival after LT for Primary Sclerosing Cholangitis (PSC). We applied a software tool, PSSP, to adult patients in the Scientific Registry of Transplant Recipients (n = 2769) who received a LT for PSC between 2002 and 2013; this produced a model for predicting individual survival distributions for novel patients. We also developed an appropriate evaluation measure, D-calibration, to validate this model. The learned PSSP model showed an excellent D-calibration (p = 1.0), and passed the single-time calibration test (Hosmer-Lemeshow p-value of over 0.05) at 0.25, 1, 5 and 10 years. In contrast, the model based on traditional Cox regression showed worse calibration on long-term survival and failed at 10 years (Hosmer-Lemeshow p value = 0.027). The calculator and visualizer are available at: http://pssp.srv.ualberta.ca/calculator/liver_transplant_2002. In conclusion we present a new tool that accurately estimates individual post liver transplantation survival.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.