Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol

IntroductionTo enhance the quality of surgical care, complications need to be minimised. Consequently, comprehending the occurrence and risk elements for postoperative complications is essential. Subsequently, we will apply machine learning (ML) algorithms to build risk factor prediction models that...

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Veröffentlicht in:	BMJ open Jg. 15; H. 10; S. e108019
Hauptverfasser:	Vatankhah Tarbebar, Majid, Mohammadi, Milad, Mehrnoush, Vahid, Darsareh, Fatemeh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England British Medical Journal Publishing Group 28.10.2025 BMJ Publishing Group LTD BMJ Publishing Group
Schlagworte:	Adult Algorithms Antibiotics Anticoagulants Artificial intelligence Blood transfusions Cardiac arrest COVID-19 vaccines Drug use Female Humans Hypertension Immunization Iran Kidney diseases Machine Learning Male Marital status Missing data Molecular weight Mortality Patients Pneumonia Postoperative Complications - diagnosis Postoperative Complications - epidemiology Postoperative Complications - etiology Prognosis Quality improvement Questionnaires Regression analysis Research Design Risk Assessment - methods Risk Factors Sepsis Surgery Variables Ventilators Iran SURGERY Machine Learning Risk Factors
ISSN:	2044-6055, 2044-6055
Online-Zugang:	Volltext
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