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Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery

Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potenti...

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Autores principales: Stam, Wessel T., Ingwersen, Erik W., Ali, Mahsoem, Spijkerman, Jorik T., Kazemier, Geert, Bruns, Emma R. J., Daams, Freek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520164/
https://www.ncbi.nlm.nih.gov/pubmed/36840764
http://dx.doi.org/10.1007/s00595-023-02662-4
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author Stam, Wessel T.
Ingwersen, Erik W.
Ali, Mahsoem
Spijkerman, Jorik T.
Kazemier, Geert
Bruns, Emma R. J.
Daams, Freek
author_facet Stam, Wessel T.
Ingwersen, Erik W.
Ali, Mahsoem
Spijkerman, Jorik T.
Kazemier, Geert
Bruns, Emma R. J.
Daams, Freek
author_sort Stam, Wessel T.
collection PubMed
description Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.
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spelling pubmed-105201642023-09-27 Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery Stam, Wessel T. Ingwersen, Erik W. Ali, Mahsoem Spijkerman, Jorik T. Kazemier, Geert Bruns, Emma R. J. Daams, Freek Surg Today Short Communication Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery. Springer Nature Singapore 2023-02-25 2023 /pmc/articles/PMC10520164/ /pubmed/36840764 http://dx.doi.org/10.1007/s00595-023-02662-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Short Communication
Stam, Wessel T.
Ingwersen, Erik W.
Ali, Mahsoem
Spijkerman, Jorik T.
Kazemier, Geert
Bruns, Emma R. J.
Daams, Freek
Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery
title Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery
title_full Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery
title_fullStr Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery
title_full_unstemmed Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery
title_short Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery
title_sort machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery
topic Short Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520164/
https://www.ncbi.nlm.nih.gov/pubmed/36840764
http://dx.doi.org/10.1007/s00595-023-02662-4
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