Blood transfusion prediction using restricted Boltzmann machines

Jenny Cifuentes, Yuanyuan Yao, Min Yan and Bin Zheng

Computer Methods in Biomechanics and Biomedical Engineering, 2020, vol. 23, issue 9, 510-517

Abstract: The availability of blood transfusion has been a recurrent concern for medical institutions and patients. Efficient management of this resource represents an important challenge for many hospitals. Likewise, rapid reaction during transfusion decisions and planning is a critical factor to maximize patient care. This paper proposes a novel strategy for predicting the blood transfusion need, based on available information, by means of Restricted Boltzmann Machines (RBM). By extracting and analyzing high-level features from 4831 patient records, RBM can deal with complex patterns recognition, helping supervised classifiers in the task of automatic identification of blood transfusion requirements. Results show that a successfully classification is obtained (96.85%), based only on available information from the patient records.

Date: 2020
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DOI: 10.1080/10255842.2020.1742709

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