A cognitive technology based healthcare monitoring system and medical data transmission. (November 2019)

• Record Type: Journal Article
• Authors: Kumar, M. Arun; Vimala, R.; Britto, K.R. Aravind
• Is Part Of: Measurement. Volume 146(2019)
• Page Start: 322
• Page End: 332
• Publication Date: 2019-11
• Subjects: Medical data processing -- Big-data -- Healthcare monitoring system -- Clustering and classification; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.03.017
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Healthcare data is increasing tremendously. Manage and transmit healthcare data using cognitive technology. Cognitive technology is incorporated using AI method. Simulated Annealing method used for data management. Abstract: In healthcare management, the volume of patient data generated from various medical devices used for patient monitoring. Increasing healthcare data is persisted in distributed database available in cloud, for sharing among medical experts. Cloud provides most excellent methods to explore the organized and unorganized data prepared from healthcare management systems. By analyzing the healthcare data, the health conditions and the treatment methods are predicted for future treatment. Yet, the existing design could not sustain both analysis and procedure for huge quantity of multi-organized healthcare data. In this paper, it is aimed to provide a cognitive technology for efficient monitoring and transmission of medical data in a healthcare industry. To do that, a Cognitive Data Transmission Method (CDTM) is proposed for monitor, record and transmit the data patient's health related data. A highest priority is assigned in data transmission, when the health condition is critical, which can be identified from the data-packet information. The cognitive nature is obtained using Simulated Annealing method. Finally, a stochastic prophesy representation is intended to predict the future health condition of the most related patients based on their present health conditions. Performance assessment of the planned procedure is recognized through general model in the cloud surroundings, that ensures 98% accurateness of prediction and sustain 99% of CPU and bandwidth consumption to decrease the analysis time