Computer algorithm can match physicians’ decisions about blood transfusions

Y. Yao, J. Cifuentes, B. Zheng, M. Yan

Journal of Translational Medicine Vol. 17, pp. 340-1 - 340-5

Summary:

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.

Keywords: Artificial intelligence; Neural networks (computer); Computer algorithm; Blood transfusion; Patient safety; Surgery

JCR Impact Factor and WoS quartile: 4,124 - Q2 (2019); 7,400 - Q1 (2022)

DOI reference: https://doi.org/10.1186/s12967-019-2085-y

Published on paper: 2019.

Published on-line: October 2019.

Citation:
Y. Yao, J. Cifuentes, B. Zheng, M. Yan, Computer algorithm can match physicians’ decisions about blood transfusions. Journal of Translational Medicine. Vol. 17, pp. 340-1 - 340-5, 2019. [Online: October 2019]