Summary:
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.
Keywords: Blood transfusion prediction; restricted Boltzmann machines; patterns recognition
JCR Impact Factor and WoS quartile: 1,763 - Q4 (2020); 1,700 - Q3 (2023)
DOI reference:
https://doi.org/10.1080/10255842.2020.1742709
Published on paper: July 2020.
Published on-line: March 2020.
Citation:
J. Cifuentes, Y. Yao, M. Yan, B. Zheng, Blood transfusion prediction using restricted Boltzmann machines. Computer Methods in Biomechanics and Biomedical Engineering. Vol. 23, nº. 9, pp. 510 - 517, July 2020. [Online: March 2020]
