NSU Research Contributions

Title : Deep Learning and Data Balancing Approaches in Mining Hospital Surveillance Data

Authors : Adnan Firoze, Tonmoay Deb, Rashedur M Rahman

Abstract : A number of classifier models on hospital surveillance data to classify admitted patients according to their critical conditions with an emphasis to deep learning paradigms, namely convolutional neural network, were used in this research. Three class labels were used to distinguish the criticality of the admitted 25,261 patients. The authors have set forth two distinct approaches to address the unbalance nature of data. They used multilayer perceptron (MLP), convolutional neural network (CNN), and multinomial logistic regression classifications and finally compared the performance of our models with the models developed by Firoze, Hasan and Rahman (2013). After comparison, the authors show that one of the models, including convolutional neural network based on deep learning, surpasses most models in terms of classification performance in contingent with training times and epochs. The trade-off is computational power for which—to achieve optimal accuracy—multiple CUDA cores are necessary. The authors achieved stable improvement of classification for their model using CNN.

Journal : Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics	Volume : 7	Year : 2018	Issue : 3
Pages : 140-212	City :	Edition :	Editors :
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