Systematic evaluation of machine learning models for postoperative surgical site infection prediction.

<h4>Background</h4>Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the prediction of this serious surgical complication have been developed, with an increasing use of machine learning (ML) tools.<h4&...

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Bibliographic Details
Published in:PLoS ONE
Main Authors: Anna M van Boekel, Siri L van der Meijden, Sesmu M Arbous, Rob G H H Nelissen, Karin E Veldkamp, Emma B Nieswaag, Kim F T Jochems, Jeroen Holtz, Annekee van IJlzinga Veenstra, Jeroen Reijman, Ype de Jong, Harry van Goor, Maryse A Wiewel, Jan W Schoones, Bart F Geerts, Mark G J de Boer
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2024-01-01
Online Access:https://doi.org/10.1371/journal.pone.0312968

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https://doi.org/10.1371/journal.pone.0312968

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