A Smart Data Pre-Processing Approach to Effective Management of Big Health Data in IoT Edge

The method is providing and overview of the organization in the management perspective, within the health big data analysis, especially for the elderly employees, the organizations could sign the elderly employees within the right tasks, it reducing the costs by increasing the employees’ job performance and organization performance. By addressing the importance role of big health data analytics (BDHA) in the healthcare system .moreover BDHA enables a patient's medical records to be searched in a dynamic, interactive manner. One billion records were made in two hours. Current clinical reporting compares large health data profiles and meta-big health data, giving health apps basic interfaces. A combination of Hadoop/MapReduce and HBase was used to generate the necessary hospital-specific large heath data. One billion (10TB) and three billion (30TB) HBase large health data files might be created in a week or a month using the concept. Apache Hadoop technologies tested simulated medical records. Inconsistencies reduced big health data. An encounter-centered big health database was difficult to set up due to the complicated medical system connections between big health data profiles. Associated with job performance such as the gender, current/past job positions and the health conditions are important. For genders the 66.36% of respondents in the experiments are females, 33.64 are males, majority of are healthy which are 66.97%, 30.58% are common geriatric disease, rest 2.45% are suffering from occupational disease; In terms of the current/past job positions, 20% of the respondents are working as accountant, followed by sales and management level. The Diagnostic and Statistical Manual, lists 157 distinct illnesses. Individuals may be diagnosed with one or more illnesses as a consequence of medical health professionals watching and analyzing their symptoms. It has been discovered that mental health issues have a negative impact on employees' job performance. For example, research on individuals with anxiety and depression has a direct impact on concentrations, decision-making process, and risk-taking behavior, which can be determined for job performance. Machine learning focuses on approaches that can be used to create accurate predictions about future characteristics based on previous training and post training. Principles such as job task and computational learning are crucial for machine learning algorithms that use a large amount of big health data.

While much has been written about the role of ‘big data’ in health services research and epidemiology, there has been less exploration of the imperative of data sovereignty on informing the ethics of health services research and global health more broadly, especially in the context of decoloniality in an era of ‘big data.’ In this viewpoint, epidemiologist and health services researcher Qato offers a brief exploration of some questions that may drive this effort: is ‘decolonizing’ health data necessary? If so, what are the stakes, and who sets the terms? What would a decolonized data infrastructure necessary for health systems equity globally look like?

Introduction: Artificial intelligence (AI) and big data have revolutionized research and education in Health Sciences between 2018 and 2022. They offer innovative tools for data analysis, medical diagnosis and treatment personalization. This article reviews the most significant advances in this field. Applications such as predictive medicine, the use of big data in public health and the integration of technologies in the training of health professionals are highlighted. Methodology: It was carried out using a documentary review approach. To do so, several scientific texts were consulted, with a predominance of original or review articles, published between 2018 and 2022. The sources came mainly from databases such as Scopus, Web of Science and Google Scholar and were processed through the bibliographic manager Zotero. Results: The COVID-19 pandemic accelerated the adoption of these tools, highlighting their potential and persistent challenges. The quality of data, teacher training and gaps in access to technological resources are highlighted; in the educational field, adaptive learning platforms and simulations based on real data have transformed teaching methods, although their implementation requires significant investments. In addition, the ethical and regulatory aspects associated with the use of AI and big data are discussed, and the need for global standards that protect privacy and avoid bias in algorithms is underlined. Conclusion: This integrative analysis provides a critical overview of how these technologies shape the present and future of health, identifying challenges and opportunities for their equitable and sustainable adoption.

Using big data analytics in healthcare has positive as well as life-saving results. Big data refers to the vast amounts of information generated by the digitization of everything that is synthesized and analyzed by specific technologies. Here Big Data uses health services to use specific health data of a population (or a specific individual) and potentially help prevent disease pandemics, treat diseases, reduce costs, and more. In the field of health, big data covers a wide range of information, including physiological, behavioral, molecular, clinical, medical imaging, disease management, medication history, nutrition, or exercise parameters. Big Data Analysis In the field of health, it is a complex process of examining big data to discover information. This information includes hidden patterns, market trends, unknown correlations, and customer preferences. Information analysis can help organizations make informed business and clinical decisions. The medical data-driven industry is the most complex among industries. Not only is this data available from a variety of sources, but it must also comply with government regulations. This process is difficult and delicate and requires some level of security and communication. Due to the importance of this issue, in this article, after introducing the types of data available in the health industry, the characteristics and sources of big data in health are defined and an analytical model for the use of large data in the health industry is presented. This model helps to understand the dimensions, components, and key elements of using big data in the health industry.

Background: Collecting and analyzing large volumes of disparate datasets are of major challenges arisen from the emergence of Big Data in the field of health. But the technology of Big Data is also associated with promising opportunities which can provide improvement of performance and facilitation of innovation in organizations. Objective: Since determining the lifetime is a practical approach regarding recognition of a phenomenon and its management, this paper aimed at identifying the challenges and opportunities of managing Big Data in the area of health at different stages of the lifecycle of Big Data. Methodology: This article is a structured review. After an initial review, 6 phases was detected in the lifecycle of Big Data, then the processes of traditional data were briefly reviewed in each phase, and the challenges associated with the emergence of Big Data and the solutions for their optimal management were discussed. Results: This study offers a broad over view of the advent of Big Data in the health sector, and provides a clear and accurate picture of the processes before and after its emergence through a comparative-based survey in each phase. The article points out innovations and modern methods of collection, pre-processing, and analysis of Big Data as well as the process of data extracting. It also describes cloud computing applications in the storage and release of Big Data. Conclusions: Our findings indicate that management of Big Data in health, based on its lifecycle, is resourceful for managers and policy-makers, in order to benefit from the technological features of Big Data with a managerial approach, to evaluate challenges, to apply innovative solutions at each phase of Big Data maturation, and to advance towards a new level of innovation, competitiveness, and productivity.

Prediction of health monitoring with deep learning using edge computing

In this paper, we are visualizing a military health service (MHS) platform which is based on hierarchical IoT architecture. We propose a semantic Edge based network model which plays a significant role for communicating tactical and non-tactical piece of information over the network. Further, the exchange of information and subsequent data analysis on the MHS makes the system intelligent and smart. In any standard battlefield scenario, there is a command and control center that correlates the events happening in real time. We have made this command and control center as semantic edge component. This center is entrusted with making vital decisions on the tactical arena of the battlefield. The main aim of the proposed architecture is to provide secured zone to monitor soldiers health and their weapons conditions, respectively. We have also introduced the semantic edge computing mechanism to deal with the large amount of health data in terms of processing, storing and sharing information.

Abstract. The Big Data, a result of the digital revolution, offers several opportunities in the field of health. Indeed, appliances and applications permanently connected to humans and the global digitalization of medical documents produce a vast health data: "Big Health Data". This data is the subject of several projects in the world given the opportunities offered to optimize this area. This paper focuses on quantifying the production of scientific articles about Big Health Data research and the most investigated Big Health Data topics. It also presents a mapping of countries producing articles about this subject. In remote sensing using real time categories, we aimed to quantify articles dealing with “big data architectures”, technologies and data sources used. A systematic mapping study was conducted with a set of seven research questions by investigating articles from two digital libraries: Scopus and Springer. The study concern articles published in 2017 and the first half of 2018. The results are illustrated by diagrams answering each question from which a set of recommendations are concluded in this area of research. The study shows that this Data is used the most in studies of oncology. Statistics show that while remote sensing and monitoring is a hot topic, real-time use is not as interesting. It was found that there’s a lack in studies interested in big data technologies used in real time remote sensing in the field of health. In conclusion, we recommend more focus on research area treating architecture in remote sensing real time Big Health Data systems combined with geolocation.

There is a lack of effective management methods to obtain the information and data of devices in transformer service areas. Based on edge computing and IoT perception technologies, this paper proposes a low-voltage smart transformer service area scheme using intelligent transformer terminal unit (TTU) and smart circuit breaker (SCB). TTU is the edge computing device which is introduced to realize the local edge computing and tasks in transformer service areas. SCB is implanted in transformer service area to collect the information and data of devices, such as PVs and EV charging piles. A practical case for PV monitoring is introduced in this paper.

Aiming at the problems of high latency and poor accuracy of retrieval response in current health data integration related research, this paper presents a health data resource integration method based on Hybrid Cloud and Fog Computing. Based on the general model of health data resource service platform, the framework of large health data resource integration is constructed. Sample reduction and dimension reduction are used to reduce big health data. The field matching method based on participle and weight is used to clean data resources and calculate the field matching degree. When the matching degree is larger than a threshold value, the fields to be matched are similar duplicate records, and the redundant data is removed. The weight of resource data is calculated by Hybrid Cloud and Fog Computing, and the big health data is arranged according to the weight value, so as to realize the classification and integration of health data.The experimental results show that the proposed method has good performance, low data redundancy, low retrieval response delay and high classification integration accuracy.

The Right Not to Be Subject to Automated Individual Decision-Making/Profiling Concerning Big Health Data. Developing an Algorithmic Culture

Modern public health faces substantial challenges toward predicting exponential rise of communicable and non-communicable diseases (NCDs), unprecedented health crises, lifestyle risk factors and environmental health hazards to populations worldwide. Public health physicians require new and efficient models and resources to tackle, detect and combat infectious disease outbreaks, rare diseases, lifestyle risk factors, mental health repercussions and maternal and child health challenges which not only impacts the overall human health system but also escalates the country’s economic burden and citizen’s quality of life. The integration of big health data, epidemiologic informatics and computational intelligence approaches has created new hopes for people and policy advocates to diagnose and predict diseases at an early stage to prevent further complications, rise in hospital admissions or increased morbidities and mortalities. This chapter will systematically appraise the application of deep learning approaches (a subset of machine learning) and big public health data utilization in predicting diseases across major domains in public health, particularly NCDs, communicable diseases, socio-behavioral medicine, maternal and child health and environmental health.

Twitter is a popular social network for people to share views or opinions on various topics. Many people search for health topics through Twitter; thus, obtaining a vast amount of social health data from Twitter is possible. Topic models are widely used for social health-care data clustering. These models require prior knowledge about the clustering tendency. Determining the number of clusters of given social health data is known as the health cluster tendency. Visual techniques, including visual assessment of the cluster tendency, cosine-based, and multiviewpoint-based cosine similarity features VAT (MVCS-VAT), are used to identify social health cluster tendencies. The recent MVCS-VAT technique is superior to others; however, it is the most expensive technique for big social health data cluster assessment. Thus, this paper aims to enhance the work of the MVCS-VAT using a sampling technique to address the big social health data assessment problem. Experimental is conducted on different health datasets for demonstrating an efficiency of proposed work. Accuracy of social health data clustering is improved at a rate of 5 to 10% in the proposed S-MVCS-VAT when compared to MVCS-VAT. From obtained results, it also proved that the proposed S-MVCS-VAT is a faster and memory efficient for discovering social health data clusters.

Technologies are changing very fast and data has an impact on the change of technology and development of world. Data are obtained by social media, the Internet and mobile technologies. For years, academics, researchers and companies utilize some sources and information to analyze them for their studies and jobs. Increasing usage of mobile devices, social networks, electronic records of customers in public and private sectors have led to increase in data. Obtained massive amount of data is called big data. There are a lot of description of big data in the literature, but simply it can be said that; big data is the data which have a massive size and can be obtained from every environment. One of these environment is health environment and it has grown fastly through that huge amount of data exist in this sector like patients’ electronic health record. Health sector has a high cost and decision will be taken as soon as possible and correctly in this sector in which timing is critically important. In this manner, the usage of big data in health is important to increase the quality of service, innovative health operations and decrease the cost. In this study, a brief review of literature has done for the use of big data in health sciences for last five years. Big data’s content, methods, advantages and difficulties are discussed in this review study. Keywords: Health science, Big data, Medicine, data mining

Understanding the construction of the social gradient in health is a major challenge in the field of social epidemiology, a branch of epidemiology that seeks to understand how society and its different forms of organization influence health at a population level. Attempting to answer these questions involves large datasets of varied heterogeneous data suggesting that Big Data approaches could be then particularly relevant to the study of social inequalities in health. Nevertheless, real challenges have to be addressed in order to make the best use of the development of Big Data in health for the benefit of all. The main purpose of this perspective is to discuss some of these challenges, in particular: (i) the perimeter and the particularity of Big Data in health, which must be broader than a vision centerd solely on care, the individual and his or her biological characteristics; (ii) the need for clarification regarding the notion of data, the validity of data and the question of causal inference for various actors involved in health, such data as researchers, health professionals and the civilian population; (iii) the need for regulation and control of data and their uses by public authorities for the common good and the fight against social inequalities in health. To face these issues, it seems essential to integrate different approaches into a close dialog, integrating methodological, societal, and ethical issues. This question cannot escape an interdisciplinary approach, including users or patients.