Towards an Intelligent Surgical Preoperative Decision Support System: An Approach Based on Decision Trees and the Random Forest
KeywordsSurgery, Preoperative phase, Artificial Intelligence, Machine Learning, Decision Tree, Random Forest
In recent years, Artificial Intelligence has experienced significant growth thanks to technological advances such as high performance computing and massive data processing. This evolution has led to a new reflection as well as a serious interest on the part of managers of hospital structures, who are beginning to take an interest in the automation and optimization of surgical processes. Machine Learning finds its place as a preferred technique for developing intelligent decision support systems in the operating room. In this aisle, it has become crucial to automate and optimize preoperative procedures. Through this paper, we focus on the development of an intelligent decision support system for the surgical preoperative phase, using the Random Forest model, which is an extension of the decision tree algorithm, to analyze a variety of preoperative predictive data such as hypertension, body temperature, ECG, hemoglobin, etc. We use a dataset previously approved by an institutional review board, while validating our model in a development environment dedicated to the field of Artificial Intelligence.
Issue: Vol. 2 No. 3 (2023) (view)
Section: Review Articles
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