Machine learning applications for the prediction of surgical site infection in neurological operations

Neurosurgical Focus
Thara TunthanathipChin Taweesomboonyat

Abstract

Surgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The purpose of this study was to compare the performance of various ML models for predicting surgical infection after neurosurgical operations. A retrospective cohort study was conducted on patients who had undergone neurosurgical operations at tertiary care hospitals between 2010 and 2017. Supervised ML algorithms, which included decision tree, naive Bayes with Laplace correction, k-nearest neighbors, and artificial neural networks, were trained and tested as binary classifiers (infection or no infection). To evaluate the ML models from the testing data set, their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as their accuracy, receiver operating characteristic curve, and area under the receiver operating characteristic curve (AUC) were analyzed. Data were available for 1471 patients in the stud...Continue Reading

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Citations

Jun 24, 2021·The Surgery Journal·Thara TunthanathipSrirat Inkate
Jul 22, 2021·Chinese Journal of Traumatology = Zhonghua Chuang Shang Za Zhi·Thara Tunthanathip, Thakul Oearsakul
Jul 17, 2021·Surgical Infections·Saskya ByerlyHaytham M A Kaafarani
Sep 17, 2021·Journal of Cancer Research and Therapeutics·Thara TunthanathipTanat Vaniyapong

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