Abstract
This article describes the method of patients identification by using neural network. Our complex filters increases recognition quality and greatly reduces computing resources needed for successful identification of a patient and can be applied to any neural network, used for identification. The resulting software is developed using only open source components and needs about 1 s to recognize a patient. Our experiment showed the effectiveness of recognition of patients not less than 99.9% with false positive ratio less than 0.00001 when using the software at the medical organization. Time interval was calculated from the first face recognition in video stream to successful identification with Euclidean metric less than 0.4. Frame rate of video stream was 25 frames per second. We measured the speed of the doctors’ work before and after the introduction of the biometric identification system for patients. The patient’s reception time is reduced by an average of 5 s. Based on 20.000 visits per month and 7 min per person on average, it is possible to take an additional 238 people a month without increasing the number of doctors.
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Evdokimov Alexey, V., Kovalenko Vasiliy, A., Kurbesov Alexandr, V., Shabanov Alexey, V. (2019). Patients Identification in Medical Organization Using Neural Network. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_87
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DOI: https://doi.org/10.1007/978-3-030-04164-9_87
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