Medical Big Data and Internet of Medical Things : Advances, Challenges and Applications
25 Oct 2018-
About: The article was published on 2018-10-25. It has received 23 citations till now. The article focuses on the topics: The Internet & Big data.
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TL;DR: The managerial implication of this article is that organizations can use the findings of the critical analysis to reinforce their strategic arrangement of smart systems and big data in the healthcare context, and hence better leverage them for sustainable organizational invention.
Abstract: Organized evaluation of various big data and smart system technology in healthcare context.Proposed a conceptual model on Big data enabled Smart Healthcare System Framework (BSHSF).We extract some depth information (some relevant examples) about advanced healthcare system.In depth study about state-of-the-art big data and smart healthcare system in parallel. In the era of big data, recent developments in the area of information and communication technologies (ICT) are facilitating organizations to innovate and grow. These technological developments and wide adaptation of ubiquitous computing enable numerous opportunities for government and companies to reconsider healthcare prospects. Therefore, big data and smart healthcare systems are independently attracting extensive attention from both academia and industry. The combination of both big data and smart systems can expedite the prospects of the healthcare industry. However, a thorough study of big data and smart systems together in the healthcare context is still absent from the existing literature. The key contributions of this article include an organized evaluation of various big data and smart system technologies and a critical analysis of the state-of-the-art advanced healthcare systems. We describe the three-dimensional structure of a paradigm shift. We also extract three broad technical branches (3T) contributing to the promotion of healthcare systems. More specifically, we propose a big data enabled smart healthcare system framework (BSHSF) that offers theoretical representations of an intra and inter organizational business model in the healthcare context. We also mention some examples reported in the literature, and then we contribute to pinpointing the potential opportunities and challenges of applying BSHSF to healthcare business environments. We also make five recommendations for effectively applying `BSHSF to the healthcare industry. To the best of our knowledge, this is the first in-depth study about state-of-the-art big data and smart healthcare systems in parallel. The managerial implication of this article is that organizations can use the findings of our critical analysis to reinforce their strategic arrangement of smart systems and big data in the healthcare context, and hence better leverage them for sustainable organizational invention.
233 citations
10 Apr 2020
TL;DR: This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques and provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.
Abstract: COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.
229 citations
01 Aug 2021
TL;DR: The findings showed that the distinctive technical aspects of BC might impressively find a solution for privacy and security problems encountering the IoT development, supply distributed storage, transparency, trust, and other support for IoT.
Abstract: Since security is the most important issue in the Internet of things (IoT) and blockchain (BC), this article aims to identify, analyze, and organize the literature about security in the Io...
15 citations
TL;DR: The state-of-the-art machine learning algorithms are studied and an attention network which can grade retinal images is proposed which is validated on a public dataset EIARG1, which is only publicly available dataset for such task as per the knowledge.
Abstract: Automatic grading of retinal blood vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on vessel width, tortuosity, and vessel branching are also used for grading. However, the automated method that directly can come to a decision by taking the fundus images got less attention. Detecting eye problems based on the tortuosity of the vessel from fundus images is a complicated task for opthalmologists. So automated grading algorithm using deep learning can be most valuable for grading retinal health. The aim of this work is to develop an automatic computer aided diagnosis system to solve the problem. This work approaches to achieve an automatic grading method that is opted using Convolutional Neural Network (CNN) model. In this work we have studied the state-of-the-art machine learning algorithms and proposed an attention network which can grade retinal images. The proposed method is validated on a public dataset EIARG1, which is only publicly available dataset for such task as per our knowledge.
11 citations
TL;DR: In this article , a real-time learning system was developed to provide infection control for residential special care contexts and in doing so, explored different crowdsourcing technologies, spatial usages, and data processing methods within the scope of smart health-care systems and environments.
Abstract: Abstract In response to the COVID-19 pandemic and the need for increased research, this study aimed to develop a real-time learning system to provide infection control for residential special care contexts and in doing so, explored different crowdsourcing technologies, spatial usages, and data processing methods within the scope of smart health-care systems and environments. Experiments were conducted in the selected special care indoor environment, which was fitted with sensors and Internet of Things devices, from which generated data were used to train Convolutional Neural Networks, Long-Short Term Memory, and Binary Layered Long-Short Term Memory neural networks. Sequential neural networks were multi-layered and configured in tandem and from these, the real-time updating learning system was developed. The system monitors the user activity and environmental data and predicts critical cases to send alerts to caregivers. Findings showed that stacking neural networks over one another increases the efficiency in updating the training data of real-time learning system. Overall, the study concludes that the developed real-time learning system is lightweight, fast, and efficient for infection control and special care at the private scale and can be multiplied at multiple nodes of larger networks of smart health services and environments.
6 citations