医疗保健生物识别的深度学习

U. Kumar, Esha Tripathi, S. Tripathi, Kapil Kumar Gupta
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引用次数: 5

摘要

医疗保健系统中的错误,如记录混淆或医疗图表混淆,导致错误的药物给病人。使用传统的、不准确的患者识别流程,管理成本、法律费用和负债等主要任务会给医疗保健行业带来高昂的成本。这可以通过生物识别技术解决。患者身份识别只需要使用生理特征,不需要登记时提供SSN、保险卡、出生日期等信息。生物识别模板可以直接映射到电子健康记录,以便在随后的访问中准确地验证个人。这项技术确保没有医疗记录可以被模仿,并为正确的患者提供正确的护理。深度学习为解决医学中出现的识别和诊断问题提供了一个平台,并可用于医疗保健生物识别,分析临床参数及其组合,用于疾病预后(如疾病预测、医学知识提取、治疗计划和支持)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning for Healthcare Biometrics
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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