Blood transfusion prediction using restricted Boltzmann machines. Issue 9 (3rd July 2020)

• Record Type: Journal Article
• Authors: Cifuentes, Jenny; Yao, Yuanyuan; Yan, Min; Zheng, Bin
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 23:Issue 9(2020)
• Page Start: 510
• Page End: 517
• Publication Date: 2020-07-03
• Subjects: Blood transfusion prediction -- restricted Boltzmann machines -- patterns recognition; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2020.1742709
• Languages: English
• ISSNs: 1025-5842

Abstract

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.