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Deep Learning for Healthcare Biometrics

Deep Learning for Healthcare Biometrics

Upendra Kumar, Esha Tripathi, Surya Prakash Tripathi, Kapil Kumar Gupta
Copyright: © 2019 |Pages: 36
ISBN13: 9781522575252|ISBN10: 1522575251|EISBN13: 9781522575269
DOI: 10.4018/978-1-5225-7525-2.ch004
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MLA

Kumar, Upendra, et al. "Deep Learning for Healthcare Biometrics." Design and Implementation of Healthcare Biometric Systems, edited by Dakshina Ranjan Kisku, et al., IGI Global, 2019, pp. 73-108. https://doi.org/10.4018/978-1-5225-7525-2.ch004

APA

Kumar, U., Tripathi, E., Tripathi, S. P., & Gupta, K. K. (2019). Deep Learning for Healthcare Biometrics. In D. Kisku, P. Gupta, & J. Sing (Eds.), Design and Implementation of Healthcare Biometric Systems (pp. 73-108). IGI Global. https://doi.org/10.4018/978-1-5225-7525-2.ch004

Chicago

Kumar, Upendra, et al. "Deep Learning for Healthcare Biometrics." In Design and Implementation of Healthcare Biometric Systems, edited by Dakshina Ranjan Kisku, Phalguni Gupta, and Jamuna Kanta Sing, 73-108. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7525-2.ch004

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Abstract

Mistakes in healthcare systems such as a mix-up of records or confusing medical charts lead to the wrong medications to patients. Major tasks such as administrative costs, legal expenses, and liabilities incur high cost to the healthcare industry using traditional, inaccurate patient identification processes. This can be resolved by biometric technology. Only physiological features can be used for patient identification to eliminate need of SSN, insurance card, or date of birth during registration. A biometric template can be directly mapped to an electronic health record to accurately authenticate individuals on subsequent visits. This technology ensures no medical records can be mimicked and the right care is provided to the right patient. Deep learning provides a platform to solve identification and diagnostic problems arising in medicine and can be used in healthcare biometrics to analyze clinical parameters and their combinations for disease prognosis (e.g., prediction of disease, extracting medical knowledge, therapy planning, and support).

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