The Intelligent Healthcare Data Management System Using Nanosensors

Dorj, Ulzii-Orshikh; Lee, Malrey; Choi, Jae‐Young; Lee, Young Keun; Jeong, Gisung

doi:10.1155/2017/7483075

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Considering the first textbox function to be T1 in Figure 8, the values from the textbox are first converted into binary format and then get concatenated with the power (2,6) values' generated constants. The constants are the keys to the hashing algorithm.…”

Section: Hashing the Medical Management Data For Securitymentioning

confidence: 99%

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Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Mohanty¹,

Das

Mohanty

et al. 2022

Healthcare

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Background: 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. Methods: 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. Results: 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.

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Section: Hashing the Medical Management Data For Securitymentioning

confidence: 99%

“…The major healthcare provider fraud happens in billing, drug, patient registration, insurance, ICU services, etc. [ 2 ]. Fraud in health insurance is increasing day by day and due to this, public-fund misutilization is also happening.…”

Section: Introductionmentioning

confidence: 99%

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Mohanty¹,

Das

Mohanty

et al. 2022

Healthcare

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“…Lee et al [10] created a management system that is appropriate for diabetic individuals based on their blood sugar levels. Dorj et al [11] created IHDMS (Intelligent Healthcare Data Management System) with nano-sensors for a mobile application. e proposed IHDMS organizes patient healthcare data from nano-sensors and converts it into the widely used HL7 (Health Level Seven) standard.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Node Selection Model Using FAHP and FTOPSIS for Data Retrieval in Ubiquitous Computing

Hassan

Ahmed

Farag

et al. 2022

Applied Computational Intelligence and Soft Computing

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Ubiquitous computing (UC) is an advanced computing concept that makes services and computing available everywhere and anytime. In UC, data lies at the heart of all UC applications, and the key technologies that are required to make UC a reality are data and task management. In this context, retrieving data is influenced by the dynamic nature of these systems in addition to human and sensor failures. So the main problem is how to select the most appropriate service provider for retrieving data. Retrieving data is a complex issue that requires an extensive evaluation process and is one of the biggest challenges in UC. In addition, not every eventuality in these systems can be predicted due to their dynamic nature. As a result, there is a strong need to address the uncertainty in context data. In this paper, to assist users to efficiently select their most preferred service provider for retrieving data, a new fuzzy integrated multicriteria decision-making model, which meets quality of context (QoC) and quality of service (QoS) and satisfies user quality requirements and needs, is proposed. The proposed model is based on four stages. In the initial stage, the identification of evaluation criteria is performed due to the varying importance of the selected criteria. In the second stage, a fuzzy Analytical Hierarchy Process (FAHP) procedure is utilized to assign importance weights to each criterion. In the third stage, the fuzzy Technique for Order Preference by Similarity of an Ideal Solution (FTOPSIS) is used to evaluate and measure the performance of each alternative. Finally, sensitivity analysis is performed to check the robustness and the applicability of the proposed model.

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“…From the perspective of an IT system, the ventilator is a data source which outputs information, meaning that it is in an abstract sense similar to other sensors. Many medical sensors, including ventilators, are engineered to produce their application layer data in the Health Level Seven protocol (HL7) [10]. The HL7 data is typically carried over an IP network, using TCP at the transport layer, to a medical electronic medical record system where it can be processed and stored for inspection.…”

Section: Physiological Monitoring Of Respiratory Parametersmentioning

confidence: 99%

Expediting assessments of database performance for streams of respiratory parameters

Gillan¹,

Novaković

Marshall

et al. 2018

Computers in Biology and Medicine

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A new methodology is proposed to compare database performance for streams of patient respiratory data from patients in an intensive care unit. New metrics are proposed through which databases may be compared both for this and similar streaming applications in the domain of the Internet of Things. Studies are reported using simulated patient data for four freely available databases. The statistical technique of non-parametric bootstrapping is used to minimise the total running time of the tests. We report mean values and bias corrected and accelerated confidence intervals for each metric and use these to compare the databases. We find that, among the four databases tested, ScaleDB is an optimum database technology when handling between 200 and 800 patients in this application, while PostgreSQL performs best outside of this range. Comparing the non-parametric bootstrapping method to a complete set of tests shows that the two approaches give results differing by a few percent.

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The Intelligent Healthcare Data Management System Using Nanosensors

Cited by 9 publications

References 21 publications

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

An Integrated Node Selection Model Using FAHP and FTOPSIS for Data Retrieval in Ubiquitous Computing

Expediting assessments of database performance for streams of respiratory parameters

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