Fri, May 31, 2024
Volume 8, Issue 1 (Continuous publishing 2022)
Iran J Neurosurg 2022, 8(1): 0-0 |
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Tunthanathip T, Sae-heng S, Oearsakul T, Kaewborisutsakul A, Taweesomboonyat C. Development and Deployment of Web Application Using Machine Learning for Predicting Intraoperative Transfusions in Neurosurgical Operations. Iran J Neurosurg 2022; 8 (1) : 24
URL: http://irjns.org/article-1-314-en.html
URL: http://irjns.org/article-1-314-en.html
Thara Tunthanathip * 
1, Sakchai Sae-heng2 , Thakul Oearsakul2 , Anukoon Kaewborisutsakul2 , Chin Taweesomboonyat3
1, Sakchai Sae-heng2 , Thakul Oearsakul2 , Anukoon Kaewborisutsakul2 , Chin Taweesomboonyat3
1- Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand , tsus4@hotmail.com
2- Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
3- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
2- Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
3- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
Abstract: (961 Views)
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.
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.
Article number: 24
Keywords: Machine learning, Prediction, Intraoperative transfusions, Neurosurgical operations, Web application
Type of Study: Research |
Subject:
Basic Neurosurgery
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Articles Copyright © The Author(s).
Owned by Guilan University of Medical Sciences.
Co-published by Negah Institute for Scientific Communication.
Contact Information:
IrJNS Office, Guilan Road Trauma Research Center, Poursina Hospital, Namjoo St, Rasht, Guilan, Iran.
Publisher Tel : +9821 45355555 , 45355000
Email: ir.journalofneurosurgery@gmail.com, info@irjns.org
Owned by Guilan University of Medical Sciences.
Co-published by Negah Institute for Scientific Communication.
Contact Information:
IrJNS Office, Guilan Road Trauma Research Center, Poursina Hospital, Namjoo St, Rasht, Guilan, Iran.
Publisher Tel : +9821 45355555 , 45355000
Email: ir.journalofneurosurgery@gmail.com, info@irjns.org
