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Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

BACKGROUND: Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learnin...

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Autores principales: Kantidakis, Georgios, Putter, Hein, Lancia, Carlo, Boer, Jacob de, Braat, Andries E., Fiocco, Marta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7667810/
https://www.ncbi.nlm.nih.gov/pubmed/33198650
http://dx.doi.org/10.1186/s12874-020-01153-1
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author Kantidakis, Georgios
Putter, Hein
Lancia, Carlo
Boer, Jacob de
Braat, Andries E.
Fiocco, Marta
author_facet Kantidakis, Georgios
Putter, Hein
Lancia, Carlo
Boer, Jacob de
Braat, Andries E.
Fiocco, Marta
author_sort Kantidakis, Georgios
collection PubMed
description BACKGROUND: Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians. METHODS: In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques. RESULTS: Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years. CONCLUSION: In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables. TRIAL REGISTRATION: Retrospective data were provided by the Scientific Registry of Transplant Recipients under Data Use Agreement number 9477 for analysis of risk factors after liver transplantation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (doi:10.1186/s12874-020-01153-1).
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spelling pubmed-76678102020-11-17 Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques Kantidakis, Georgios Putter, Hein Lancia, Carlo Boer, Jacob de Braat, Andries E. Fiocco, Marta BMC Med Res Methodol Research Article BACKGROUND: Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians. METHODS: In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques. RESULTS: Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years. CONCLUSION: In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables. TRIAL REGISTRATION: Retrospective data were provided by the Scientific Registry of Transplant Recipients under Data Use Agreement number 9477 for analysis of risk factors after liver transplantation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (doi:10.1186/s12874-020-01153-1). BioMed Central 2020-11-16 /pmc/articles/PMC7667810/ /pubmed/33198650 http://dx.doi.org/10.1186/s12874-020-01153-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Kantidakis, Georgios
Putter, Hein
Lancia, Carlo
Boer, Jacob de
Braat, Andries E.
Fiocco, Marta
Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques
title Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques
title_full Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques
title_fullStr Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques
title_full_unstemmed Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques
title_short Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques
title_sort survival prediction models since liver transplantation - comparisons between cox models and machine learning techniques
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7667810/
https://www.ncbi.nlm.nih.gov/pubmed/33198650
http://dx.doi.org/10.1186/s12874-020-01153-1
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