Cifuentes Jenny, Yao Yuanyuan, Yan Min, Zheng Bin
Santander Big Data Institute, Universidad Carlos III de Madrid, Getafe, Spain.
Department of Anesthesiology, Hospital of Zhejiang University, Hangzhou, China.
Comput Methods Biomech Biomed Engin. 2020 Jul;23(9):510-517. doi: 10.1080/10255842.2020.1742709. Epub 2020 Mar 24.
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.
输血的可及性一直是医疗机构和患者反复关注的问题。对这一资源进行有效管理对许多医院来说是一项重大挑战。同样，在输血决策和规划过程中的快速反应是实现患者护理最大化的关键因素。本文提出了一种基于可用信息，借助受限玻尔兹曼机（RBM）预测输血需求的新策略。通过从4831份患者记录中提取和分析高级特征，RBM能够处理复杂的模式识别，辅助监督分类器完成自动识别输血需求的任务。结果表明，仅基于患者记录中的可用信息就能成功实现分类（准确率达96.85%）。
