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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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 |
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Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2024-01-01
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Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0312968 |
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