Computer algorithm can match physicians' decisions about blood transfusions
DOI
https://doi.org/10.1186/s12967-019-2085-y
Document Type
Article
Publication Date
10-10-2019
Publication Title
Journal of Translational Medicine
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.
Volume
17
Issue
1
Recommended Citation
Yao, Yuanyuan; Cifuentes, Jenny; Zheng, Bin; and Yan, Min, "Computer algorithm can match physicians' decisions about blood transfusions" (2019). Scopus Unisalle. 125.
https://ciencia.lasalle.edu.co/scopus_unisalle/125
PubMed ID
31601245
Identifier
SCOPUS_ID:85073098353