Artificial Neural Network Medical Decision Support Tool: Predicting Transfusion Requirements of ER Patients

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Backpropagation, neural network, transfusion, trauma, artificial neural networks, erbium, blood, predictive models, hospitals, costs, surgery, medical diagnostic imaging

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Blood product transfusion is a financial concern for hospitals and patients. Efficient utilization of this dwindling resource is a critical problem if hospitals are to maximize patient care while minimizing costs. Traditional statistical models do not perform well in this domain. An additional concern is the speed with which transfusion decisions and planning can be made. Rapid assessment in the emergency room (ER) necessarily limits the amount of usable information available (with respect to independent variables available). This study evaluates the efficacy of using artificial neural networks (ANNs) to predict the transfusion requirements of trauma patients using readily available information. A total of 1016 patient records are used to train and test a backpropagation neural network for predicting the transfusion requirements of these patients during the first 2, 2-6, and 6-24 h, and for total transfusions. Sensitivity and specificity analysis are used along with the mean absolute difference between blood units predicted and units transfused to demonstrate that ANNs can accurately predict most ER patient transfusion requirements, while only using information available at the time of entry into the ER.

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Citation / Publisher Attribution

IEEE Transactions on Information Technology in Biomedicine, v. 9, issue 3, p. 468-474