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.

中文翻译：

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使用受限玻尔兹曼机进行输血预测

摘要 输血的可用性一直是医疗机构和患者反复关注的问题。对该资源的有效管理是许多医院面临的一项重要挑战。同样，在输血决策和计划期间的快速反应是最大化患者护理的关键因素。本文基于可用信息，通过受限玻尔兹曼机 (RBM) 提出了一种预测输血需求的新策略。通过从 4831 条患者记录中提取和分析高级特征，RBM ​​可以处理复杂的模式识别，帮助监督分类器完成输血需求的自动识别任务。结果表明，仅基于患者记录中的可用信息，就获得了成功的分类 (96.85%)。