ArticlePloS one2021
Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- A donor-recipient ranking model to optimize long-term survival post liver transplant.Scientific reports · 2026Article
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Machine Learning for Predicting 12-Month Survival after Liver Transplantation: Temporal Validation, Calibration, and Decision-Analytic Evaluation.Computational and structural biotechnology journal · 2026Article
- Review
- A data-driven framework for fair and efficient organ transplantation using gradient boosting and adaptive genetic allocation.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2025Article
- Review
- Perspectives and Tools in Liver Graft Assessment: A Transformative Era in Liver Transplantation.Biomedicines · 2025Review
- Advanced prognostic modeling with deep learning: assessing long-term outcomes in liver transplant recipients from deceased and living donors.Journal of translational medicine · 2025Article
- Review
- Machine learning in liver surgery: Benefits and pitfalls.World journal of clinical cases · 2024Article
- Maximizing utility of nondirected living liver donor grafts using machine learning.Frontiers in immunology · 2023Article
- Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor-Recipient Matching?Medicina (Kaunas, Lithuania) · 2022Review
- Review
- CONUT Score Predicts Early Morbidity After Liver Transplantation: A Collaborative Study.Frontiers in nutrition · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a fair comparison, the United Network for Organ Sharing database was used with 4 different end-points (3 months, and 1, 2 and 5 years), with a total of 39, 189 D-R pairs and 28 donor and recipient variables. Modelling techniques were divided into two groups: 1) classical statistical methods, including Logistic Regression (LR) and Naïve Bayes (NB), and 2) standard machine learning techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB) or Support Vector Machines (SVM), among others. The methods were compared with standard scores, MELD, SOFT and BAR. For the 5-years end-point, LR (AUC = 0.654) outperformed several machine learning techniques, such as MLP (AUC = 0.599), GB (AUC = 0.600), SVM (AUC = 0.624) or RF (AUC = 0.644), among others. Moreover, LR also outperformed standard scores. The same pattern was reproduced for the others 3 end-points. Complex machine learning methods were not able to improve the performance of liver allocation, probably due to the implicit limitations associated to the collection process of the database.
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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.