Big Data Science and Its Applications in Biomedical Research and Healthcare: A Review (original) (raw)
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Big Data Science and Its Applications in Health and Medical Research: Challenges and Opportunities
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Recently, Big Data science has been a hot topic in the scientific, industrial and the business worlds. The healthcare and biomedical sciences have rapidly become data-intensive as investigators are generating and using large, complex, high dimensional, and diverse domain specific datasets. This paper provides a general survey of recent progress and advances in Big Data science, healthcare, and biomedical research. Big Data science impacts, important features, infrastructures, and basic and advanced analytical tools are presented in detail. Additionally, various challenges, debates, and opportunities inside this quickly emerging scientific field are explored. The human genome research, one of the most promising medical and health areas as an example and application of Big Data science, is discussed to demonstrate how the adaptive advanced computational analytical tools could be utilized for transforming millions of data points into predictions and diagnostics for precision medicine and personalized healthcare with better patient outcomes.
Big Data: A Challenging Opportunity for Biomedical Informatics
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In the current technological world big data technologies are being used in bio-informatics research and healthcare. The huge amount of clinical data have been generated and collected at an unoccupied speed and scale. For example, the number of sequencing technologies in the new era producing the trillions of DNA sequence data per day, and the different applications of EHRs-Electronic health records are specifying huge amount of patient data. The amount of processing and analyzing healthcare data is about to decrease dramatically with the help of available technologies. The Big data applications provide new opportunities to enhance new knowledge and establish different type methods to improve the quality of existing healthcare system. The objective of the paper is to evaluate the applications of analytics of Big Data in the biomedicine and healthcare field and the associated outcomes.
Big data analytics for healthcare
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The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.
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Universally, the volume of data has increased, with the collection rate doubling every 40 months, since the 1980s. “Big data” is a term that was introduced in the 1990s to include data sets too large to be used with common software. Medicine is a major field predicted to increase the use of big data in 2025. Big data in medicine may be used by commercial, academic, government, and public sectors. It includes biologic, biometric, and electronic health data. Examples of biologic data include biobanks; biometric data may have individual wellness data from devices; electronic health data include the medical record; and other data demographics and images. Big data has also contributed to the changes in the research methodology. Changes in the clinical research paradigm has been fueled by large-scale biological data harvesting (biobanks), which is developed, analyzed, and managed by cheaper computing technology (big data), supported by greater flexibility in study design (real-world data)...
A Review on Big Data Application in Health Care
Big data technologies are progressively utilized for biomedical and health-care informatics research. A lot of biological and clinical data have been created and gathered at a phenomenal speed and scale. For instance, the new age of sequencing technologies empowers the handling of billions of DNA sequence data every day, and the application of electronic health records (EHRs) is archiving a lot of patient data. The cost of getting and breaking down biomedical data is required to diminish drastically with the assistance of innovation redesigns, for example, the rise of new sequencing machines, the advancement of novel equipment and programming for parallel computing, and the broad extension of EHRs. Big data applications introduce new chances to find new information and make novel strategies to enhance the nature of health care. The application of big data in health care is a quickly developing field, with numerous new disclosures and philosophies distributed over the most recent five years. In this paper, we review and talk about big data application in four noteworthy biomedical sub disciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) general health informatics. In particular, in bioinformatics, high-throughput tests encourage the research of new expansive affiliation investigations of diseases, and with clinical informatics, the clinical field benefits from the immense measure of gathered patient data for settling on smart choices. Imaging informatics is presently more quickly incorporated with cloud stages to share medical image data and work processes, and general health informatics use big data methods for foreseeing and observing infectious disease flare-ups, for example, Ebola. In this paper, we review the current advance and achievements of big data applications in these health-care domains and condense the difficulties, holes, and chances to enhance and progress big data applications in health care.
Big data -a review in health sciences
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.
IJERT-Big Data Analytics in Healthcare: A Review
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/big-data-analytics-in-healthcare-a-review https://www.ijert.org/research/big-data-analytics-in-healthcare-a-review-IJERTV10IS060198.pdf In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various sectors around the world and, neither of this data is homogeneous. This enormous amount of data, referred to as 'big data', has started to play a pivotal role in the evolution of healthcare practices and research. In this paper, we discuss how by rapid digitalization along with other factors, the health industry has been confronted with the need to handle the big data being produced rapidly at an exponential speed. Big data analytics tools play an essential role to analyze and integrate large volumes of data, which otherwise might have become useless or taken more time to give value. The usage and challenges of big data in healthcare is also addressed.
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 2022
The healthcare industry has been using data and technology driven innovation for a long time now with Evidence Based Medicine (EBM) being at the helm. Effectively using technology is vital towards successful data management in healthcare. A convenient approach that can collect, store, evaluate, and analyze health data will be beneficial to all the stakeholders in the healthcare delivery chain. With the advancement, clubbed with the availability and affordability of technology, it has become an effective tool for providing the right- care backed with accurate, consolidated evidence. Such approach not only helps in improving the quality of care, it also helps in early detection of diseases and effective treatment, Big Data is providing the necessary data management tools for the same. A simple Extract-Transform-Load (ETL) procedure using Hadoop can be used. As Big data analyses large amounts of data to uncover hidden patterns, correlations and other insights, its role in making the ri...
An overview of Big Data in Healthcare: multiple angle analyses
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Big data have been in use since the 1990s, which usually include some complex data sets whose sizes are beyond the ability of commonly used software to handle within a reasonable period of time. In recent years, big data analytics by providing personalized medicine and regulation analysis, providing clinical risk intervention and forecast analysis, reducing waste and nursing patients with external and internal variability, standardization of medical terminology and patient registration, and fragmentation of the solution, help to improve health care. This paper provides an overview of the contents of big data healthcare. We summarize some kinds of medical big data, including the electronic health records, the medical image data, the healthcare system big data, the health Internet of Things and healthcare informatics, the remote medical monitoring big data, the biomedical big data, and other sources of big data. Furthermore, we discuss some methods for handling different kinds of medi...
Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review
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Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing m...