Computer algorithm can match physicians’ decisions about blood transfusions

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.