Journal of translational medicine · 2019
Computer algorithm can match physicians' decisions about blood transfusions.
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Yao Yuanyuan, Cifuentes Jenny, Zheng Bin, Yan Min
Аннотация
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.
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.