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Prediction of massive transfusions in neurosurgical operations using machine learning
Chin Taweesomboonyat, Anukoon Kaewborisutsakul, Kanisorn Sungkaro
Department of Surgery, Division of Neurosurgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
Correspondence Address:
Kanisorn Sungkaro,
Department of Surgery, Division of Neurosurgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110
Thailand
 Source of Support: None, Conflict of Interest: None DOI: 10.4103/ajts.ajts_42_22
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INTRODUCTION: Massive blood loss and transfusions may be associated with mortality. However, blood donations have been disrupted globally, particularly in the current pandemic era. At present, machine learning (ML) is one of the modern approaches used to predict clinical outcomes. The main objective of this study was to forecast massive packed red cell (PRC) transfusions using ML. In addition, a secondary objective focused on developing an ML-based web application to assist neurosurgeons with high-risk patients and to allocate limited resources effectively during the pandemic.
METHODS: This was a historical cohort study involving individuals who had undergone neurosurgical operations. Using a 70:30 splitting method, the total dataset was randomly separated into a training dataset and a testing dataset. The supervised ML models of various algorithms were trained, whereas the testing dataset was used to validate the ML models.
RESULTS: Among 3006 neurosurgical patients, massive PRC transfusion was observed in 4.26%. The preoperative factors significantly associated with massive transfusions were used to train the ML models. For the testing process, naive Bayes (NB) algorithms had the highest performance for massive transfusion prediction with area under the curve of 0.75 (95% confidence interval: 0.72–0.80). Therefore, the NB model was deployed as a web application, which is a simple-to-use tool via https://psuneurosx.shinyapps.io/massive_transfusion/.
CONCLUSIONS: Massive PRC transfusions in neurosurgical operations occurred in 4.26%. NB algorithms showed acceptable predictability for massive PRC transfusions via the simple web application, though it may be a challenge to implement in general practice as a computerized clinical decision support system.
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