Computer algorithm can match physicians’ decisions about blood transfusions - Yuanyuan Yao; Jenny Cifuentes; Bin Zheng; Min Yan (Journal of Translational Medicine, 2019)
Abstract Background Checking appropriateness of blood transfusion for quality assurance required enormous usage of time and human resources from the healthcare system. We report here a new machine learning algorithm for checking blood transfusion quality. Materials and methods The multilayer perceptron neural network (MLPNN) was designed to learn an expert’s judgement from 4946 clinical cases. The accuracy in predicting the blood transfusion was then reported. Results We achieved a 96.8% overall accuracy rate, with a 99% match rate to the experts’ judgement on those appropriate cases and 90.9% on the inappropriate cases. Conclusions Machine learning algorithm can accurately match to human judgement by feeding in pre-surgical information and key laboratory variables.