Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions
Author:
Publisher
Springer Science and Business Media LLC
Subject
Surgery
Link
https://link.springer.com/content/pdf/10.1007/s00268-021-06360-5.pdf
Reference21 articles.
1. Vester-Andersen M, Lundstrom LH, Moller MH et al (2014) Mortality and postoperative care pathways after emergency gastrointestinal surgery in 2904 patients: a population-based cohort study. Br J Anaesth 112:860–870. https://doi.org/10.1093/bja/aet487
2. Bergenfelz A, Søreide K (2014) Improving outcomes in emergency surgery. Br J Surg. https://doi.org/10.1002/bjs.9347
3. Tengberg LT, Cihoric M, Foss NB et al (2017) Complications after emergency laparotomy beyond the immediate postoperative period–a retrospective, observational cohort study of 1139 patients. Anaesthesia 72:309–316. https://doi.org/10.1111/anae.13721
4. Saunders DI, Murray D, Pichel AC et al (2012) Variations in mortality after emergency laparotomy: the first report of the UK emergency laparotomy network. Br J Anaesth 109:368–375. https://doi.org/10.1093/bja/aes165
5. Stewart B, Khanduri P, McCord C et al (2014) Global disease burden of conditions requiring emergency surgery. Br J Surg 101:9–22. https://doi.org/10.1002/bjs.9329
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2. Prospective multicenter external validation of postoperative mortality prediction tools in patients undergoing emergency laparotomy;Journal of Trauma and Acute Care Surgery;2023-02-02
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