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Computer algorithm can match physicians' decisions about blood transfusions
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OA
AI
DOI：10.1186/s12967-019-2085-y.png)
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摘要
En 中文
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.
Keyword:
Artificial intelligence
Neural networks (computer)
Computer algorithm
Blood transfusion
Patient safety
Surgery
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期刊
IF:
7.5
论文数:
9.6K
被引数:
3.2W
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