Iranian Journal of Neurosurgery

en Development and Deployment of Web Application Using Machine Learning for Predicting Intraoperative Transfusions in Neurosurgical Operations Basic Neurosurgery Research <div style="text-align: justify;">Background and Aim: Preoperative blood product preparation is a common practice in neurosurgical patients. However, over-requesting of blood is common and leads to the wastage of blood bank resources. Machine learning (ML) is currently one of the novel computational data analysis methods for assisting neurosurgeons in their decision-making process. The objective of the present study was to use machine learning to predict intraoperative packed red cell transfusion. Additionally, a secondary objective focused on estimating the effectiveness of blood utilization in neurosurgical operations. Methods and Materials/Patients: This was a retrospective cohort study of 3,021 patients who had previously undergone neurosurgical operations. Data from the total cohort were randomly divided into a training dataset (N=2115) and a testing dataset (N=906). The supervised ML models of various algorithms were trained and tested with test data using both classification and regression algorithms. Results: Almost all neurosurgical conditions had a cross-match to transfusion ratio of more than 2.5. Support vector machine (SVM) with linear kernel, SVM radial kernel, and random forest (RF) classification had a performance with good AUC of 0.83,0.82, and 0.82, respectively, while RF regression had the lowest root mean squared error and mean absolute error. Conclusion: In almost all neurosurgical surgeries, preoperative overpreparation of blood products was detected. The ML algorithm was proposed as a high-performance method for optimizing blood preparation and intraoperative consumption. Furthermore, ML has the potential to be incorporated into clinical practice as a calculator for the optimal cross-match to transfusion ratio.  </div> Machine learning, Prediction, Intraoperative transfusions, Neurosurgical operations, Web application 0 0 http://irjns.org/browse.php?a_code=A-10-479-1&slc_lang=en&sid=1 Thara Tunthanathip tsus4@hotmail.com 10031947532846009295 10031947532846009295 Yes Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand Sakchai Sae-heng 10031947532846009296 10031947532846009296 No Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand Thakul Oearsakul 10031947532846009297 10031947532846009297 No Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand Anukoon Kaewborisutsakul 10031947532846009298 10031947532846009298 No Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand Chin Taweesomboonyat 10031947532846009299 10031947532846009299 No Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand