Blood transfusion prediction using restricted Boltzmann machines.
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.
['Cifuentes|Jenny|J|', 'Yao|Yuanyuan|Y|', 'Yan|Min|M|', 'Zheng|Bin|B|']
D000465:Algorithms D001803:Blood Transfusion D003663:Decision Trees D006801:Humans D016571:Neural Networks, Computer D015203:Reproducibility of Results
Blood transfusion prediction restricted Boltzmann machines patterns recognition