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http://dx.doi.org/10.5407/jksv.2021.19.1.057

Forecasting of erythrocyte sedimentation rate using gated recurrent unit (GRU) neural network  

Lee, Jaejin (School of Mechanical Engineering, PNU)
Hong, Hyeonji (Eco-friendly Smart Ship Parts Technology Innovation Center, PNU)
Song, Jae Min (Department of Oral and Maxilofacial Surgery)
Yeom, Eunseop (School of Mechanical Engineering, Pusan National University (PNU))
Publication Information
Journal of the Korean Society of Visualization / v.19, no.1, 2021 , pp. 57-61 More about this Journal
Abstract
In order to determine erythrocyte sedimentation rate (ESR) indicating acute phase inflammation, a Westergren method has been widely used because it is cheap and easy to be implemented. However, the Westergren method requires quite a long time for 1 hour. In this study, a gated recurrent unit (GRU) neural network was used to reduce measurement time of ESR evaluation. The sedimentation sequences of the erythrocytes were acquired by the camera and data processed through image processing were used as an input data into the neural network models. The performance of a proposed models was evaluated based on mean absolute error. The results show that GRU model provides best accurate prediction than others within 30 minutes.
Keywords
ESR; GRU; Erythocyte aggregation;
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