scholarly journals Big Data in the Health Sector

2016 ◽  
pp. 179-194 ◽  
Author(s):  
Sonja Zillner ◽  
Sabrina Neururer
Keyword(s):  
Big Data ◽  
Health Sector

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

Analysis of Big Data Analytics in Healthcare Sector: Applications and Tools

2020 ◽  
Vol 17 (12) ◽  
pp. 5605-5612
Author(s):  
A. Kaliappan ◽  
D. Chitra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
High Volume ◽  
Healthcare Sector ◽  
Genome Database ◽  
Tremendous Amount ◽  
The Impact ◽  
Different Sources

In today’s world, an immense measure of information in the form of unstructured, semi-structured and unstructured is generated by different sources all over the world in a tremendous amount. Big data is the termed coined to address these enormous amounts of data. One of the major challenges in the health sector is handling a high-volume variety of data generated from diverse sources and utilizing it for the wellbeing of human. Big data analytics is one of technique designed to operate with monstrous measures of information. The impact of big data in healthcare field and utilization of Hadoop system tools for supervising the big data are deliberated in this paper. The big data analytics role and its theoretical and conceptual architecture include the gathering of diverse information’s such as electronic health records, genome database and clinical decisions support systems, text representation in health care industry is investigated in this paper.

scholarly journals Cross-Sectoral Big Data

2019 ◽  
Vol 11 (3) ◽  
pp. 327-339 ◽  
Author(s):  
Graeme T. Laurie
Keyword(s):  
Big Data ◽  
Ethical Issues ◽  
Health Sector ◽  
Well Being ◽  
Biomedical Data ◽  
Ethical Values ◽  
Ethics Framework ◽  
Full Explanation ◽  
Health And Well Being

Abstract Discussion of uses of biomedical data often proceeds on the assumption that the data are generated and shared solely or largely within the health sector. However, this assumption must be challenged because increasingly large amounts of health and well-being data are being gathered and deployed in cross-sectoral contexts such as social media and through the internet of (medical) things and wearable devices. Cross-sectoral sharing of data thus refers to the generation, use and linkage of biomedical data beyond the health sector. This paper considers the challenges that arise from this phenomenon. If we are to benefit fully, it is important to consider which ethical values are at stake and to reflect on ways to resolve emerging ethical issues across ecosystems where values, laws and cultures might be quite distinct. In considering such issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of cross-sectoral big data. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

scholarly journals Big data - a review in health sciences

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Ayça Kurnaz Türkben ◽  
Emre Türkben ◽  
Dilek Karahoca ◽  
Adem Karahoca
Keyword(s):  
Big Data ◽  
Health Sector ◽  
Health Sciences ◽  
Health Science ◽  
Mobile Technologies ◽  
Review Of Literature ◽  
Public And Private ◽  
Review Study ◽  
The Cost

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

Big data and machine learning in health

2020 ◽  
Vol 30 (Supplement_2) ◽  
Author(s):  
D Carvalho ◽  
R Cruz
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Scientific Literature ◽  
Health Sector ◽  
Clinical Practices ◽  
Huge Amount ◽  
Cancer Breast ◽  
A Value ◽  
Appropriate Treatment

Abstract Introduction Big data is defined as the amount of data that once organized and analysed, can make a value, make decisions, make predictions and discover patterns in order to reduce costs, avoid risks and optimize services. Machine Learning (ML) is a field of artificial intelligence and is characterized as a method of machine learning, which uses algorithms that learn from data analysis, allowing computers to find patterns, draw conclusions and make predictions. These tools can be used in different areas of human knowledge, particularly in the health sector which are generated daily a huge amount of information, allowing the creation of algorithms that learn and gain understanding to assist in various clinical practices. Objectives The purpose of this paper is to analyse the benefits of Big Data and Machine Learning in providing overall health care. Methodology We conducted a review of the scientific literature published in the electronic databases PubMed/MEDLINE and Google Scholar, according to specific criteria, using keywords: Big Data”, “Machine Learning". Results In the field of oncology (skin cancer, breast, lung, leukaemia) ML and Big Data have contributed to early diagnosis of different pathologies and their evolution, as well as optimizing therapies. In ophthalmology (diabetic retinopathy and congenital cataract) has shown high efficacy in rapid diagnosis and appropriate treatment crucial to prevent the progress of the disease. The tested algorithms achieved very favourable results in cases of Parkinson’s and cardiovascular diseases. In the pharmaceutical industry these computer and digital tools have contributed to the optimization of clinical trials, genome sequencing of tumours to then identify and develop specific drugs to fight it. Conclusion Advances of MIL and Big data are notorious and development opportunities are immense and can come to revolutionize tasks such as diagnosis, treatment and health care in general.

scholarly journals Public Health and Epidemiology Informatics

2016 ◽  
Vol 25 (01) ◽  
pp. 240-246 ◽  
Author(s):  
A. Bar-Hen ◽  
N. Paragios ◽  
A. Flahault
Keyword(s):  
Public Health ◽  
Big Data ◽  
Health Sector ◽  
Point Of View ◽  
General Point ◽  
Full Potential ◽  
Web Based ◽  
Challenging Tasks ◽  
Epidemiology Data ◽  
Using Data

Summary Objectives: The aim of this manuscript is to provide a brief overview of the scientific challenges that should be addressed in order to unlock the full potential of using data from a general point of view, as well as to present some ideas that could help answer specific needs for data understanding in the field of health sciences and epidemiology. Methods: A survey of uses and challenges of big data analyses for medicine and public health was conducted. The first part of the paper focuses on big data techniques, algorithms, and statistical approaches to identify patterns in data. The second part describes some cutting-edge applications of analyses and predictive modeling in public health. Results: In recent years, we witnessed a revolution regarding the nature, collection, and availability of data in general. This was especially striking in the health sector and particularly in the field of epidemiology. Data derives from a large variety of sources, e.g. clinical settings, billing claims, care scheduling, drug usage, web based search queries, and Tweets. Conclusion: The exploitation of the information (data mining, artificial intelligence) relevant to these data has become one of the most promising as well challenging tasks from societal and scientific viewpoints in order to leverage the information available and making public health more efficient.

scholarly journals An Application Overview of IoT Enabled-Big Data Analytics in Health Sector with Special Reference to Covid-19

2021 ◽  
Author(s):  
RAJIB BISWAS
Keyword(s):  
Big Data ◽  
Special Reference ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Space Communications ◽  
Future Expectations ◽  
Computing Systems ◽  
Day By Day

— Big Data analytics has come a long way since its inception. This field is growing day by day. With the advent of large handling capacity of computational analysis of modern computing systems as well as Internet of Things (IoT), this field has revolutionized the way we think about data. It has influenced the major domains such as healthcare, automobile, computing, climatology, and space communications. Of late, the health care sector has been largely influenced by this. This communication deals with the areas of healthcare where big data analytics has been largely influential. Encompassing the basics of Big Data Analytics (BDA) driven by IoT, the applications of it in healthcare sector are outlined, accompanied by future expectations. Additionally, it also presents a comprehensive analysis of recent application with special reference to Covid-19 in this sector.

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