Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1785109853886218240 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10520164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT stamwesselt machinelearningmodelsinclinicalpracticeforthepredictionofpostoperativecomplicationsaftermajorabdominalsurgery AT ingwersenerikw machinelearningmodelsinclinicalpracticeforthepredictionofpostoperativecomplicationsaftermajorabdominalsurgery AT alimahsoem machinelearningmodelsinclinicalpracticeforthepredictionofpostoperativecomplicationsaftermajorabdominalsurgery AT spijkermanjorikt machinelearningmodelsinclinicalpracticeforthepredictionofpostoperativecomplicationsaftermajorabdominalsurgery AT kazemiergeert machinelearningmodelsinclinicalpracticeforthepredictionofpostoperativecomplicationsaftermajorabdominalsurgery AT brunsemmarj machinelearningmodelsinclinicalpracticeforthepredictionofpostoperativecomplicationsaftermajorabdominalsurgery AT daamsfreek machinelearningmodelsinclinicalpracticeforthepredictionofpostoperativecomplicationsaftermajorabdominalsurgery |