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Machine learning based prediction of perioperative blood loss in orthognathic surgery
پیش بینی مبتنی بر یادگیری ماشین در مورد از دست دادن خون پس از عمل در جراحی ارتوژناتیک-2019 The aim of this study was to evaluate, if and with what accuracy perioperative blood loss can be
calculated by a machine learning algorithm prior to orthognathic surgery. The investigators
implemented a random forest algorithm to predict perioperative blood loss. 1472 patients who
underwent orthognathic surgery from 01/2006 to 06/2017 at our institution were screened and
950 patients were included and separated 80%/20% in a training set - utilized to generate the
prediction model - and a testing set - utilized to estimate the accuracy of the model. The outcome
variable was the correlation between actual perioperative blood loss and predicted perioperative
blood loss in the testing set. Other study variables were the difference of actual and predicted
perioperative blood loss and important factors influencing perioperative blood loss using random
forest feature importance. Descriptive and bivariate statistics were computed and the P value was
set at 0.05. There was a statistically significant correlation between actual perioperative blood
loss and predicted perioperative blood loss (p<0.001). The mean difference was 7.4 ml with a
standard deviation of 172.3ml. The results of this study suggest that the application of a
machine-learning algorithm allows a prediction of perioperative blood loss prior to orthognathic
surgery. Keywords: orthognathic surgery| blood loss | machine learning |
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