Title

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

PubMed ID

31601245

Identifier

SCOPUS_ID:85073098353

Documento original

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