Mohanty Mohan Debarchan, Das Abhishek, Mohanty Mihir Narayan, Altameem Ayman, Nayak Soumya Ranjan, Saudagar Abdul Khader Jilani, Poonia Ramesh Chandra
Department of Electrical Engineering, Campus 1, Technische Universität, 21073 Hamburg, Germany.
Department of Electronics and Communication Engineering, Institute of Technical Education and Research (ITER), Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar 701030, India.
Healthcare (Basel). 2022 Jul 9;10(7):1275. doi: 10.3390/healthcare10071275.
The modern era of human society has seen the rise of a different variety of diseases. The mortality rate, therefore, increases without adequate care which consequently causes wealth loss. It has become a priority of humans to take care of health and wealth in a genuine way.
In this article, the authors endeavored to design a hospital management system with secured data processing. The proposed approach consists of three different phases. In the first phase, a smart healthcare system is proposed for providing an effective health service, especially to patients with a brain tumor. An application is developed that is compatible with Android and Microsoft-based operating systems. Through this application, a patient can enter the system either in person or from a remote place. As a result, the patient data are secured with the hospital and the patient only. It consists of patient registration, diagnosis, pathology, admission, and an insurance service module. Secondly, deep-learning-based tumor detection from brain MRI and EEG signals is proposed. Lastly, a modified SHA-256 encryption algorithm is proposed for secured medical insurance data processing which will help detect the fraud happening in healthcare insurance services. Standard SHA-256 is an algorithm which is secured for short data. In this case, the security issue is enhanced with a long data encryption scheme. The algorithm is modified for the generation of a long key and its combination. This can be applicable for insurance data, and medical data for secured financial and disease-related data.
The deep-learning models provide highly accurate results that help in deciding whether the patient will be admitted or not. The details of the patient entered at the designed portal are encrypted in the form of a 256-bit hash value for secured data management.
人类社会的现代时期出现了各种各样不同的疾病。因此，在缺乏充分护理的情况下死亡率会上升，进而导致财富损失。切实地关注健康和财富已成为人类的当务之急。
在本文中，作者致力于设计一个具有安全数据处理功能的医院管理系统。所提出的方法包括三个不同阶段。在第一阶段，提出了一种智能医疗系统，用于提供有效的医疗服务，特别是针对脑肿瘤患者。开发了一个与安卓和基于微软的操作系统兼容的应用程序。通过这个应用程序，患者可以亲自或从远程地点进入系统。结果，患者数据仅在医院和患者之间得到保护。它包括患者登记、诊断、病理学、入院和保险服务模块。其次，提出了基于深度学习从脑部磁共振成像（MRI）和脑电图（EEG）信号中检测肿瘤的方法。最后，提出了一种改进的SHA-256加密算法用于安全的医疗保险数据处理，这将有助于检测医疗保险服务中发生的欺诈行为。标准的SHA-256是一种适用于短数据的安全算法。在这种情况下，通过长数据加密方案增强了安全性问题。该算法经过修改以生成一个长密钥及其组合。这可适用于保险数据以及用于安全金融和疾病相关数据的医疗数据。
深度学习模型提供了高度准确的结果，有助于决定患者是否入院。在设计的门户中输入的患者详细信息以256位哈希值的形式进行加密，以实现安全的数据管理。
