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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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Detalles Bibliográficos
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
Descripción
Sumario: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.