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There is a need of data integration in cloud – based system, we propose an Information Integration and
Informatics framework for cloud – based healthcare application. The data collected by the Electronic
Health Record System need to be smart and connected, so we use informatica for the connection of data
from different database. Traditional Electro...
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Citations
... Fraud Detection: 'Suspect, detect and protect'. Fraud, waste, and abuse have caused significant cost and it range from honest mistakes that result in erroneous [282,133,97,280,170] Data and Workflow Sharing [20,50,221,221] Data Analysis [68,170,263] Bioinformatics Feature Selection [13,16,288,293,255,165] Classification [92,25,284,37,107,106,293,56,66] Clustering [142,189,79,119,279,46,199] Microarray Data Analysis [249,201,162,57,156] Protein-Protein Interaction [219,12,187,144] Pathway Analysis [287,130,281,99,198,166] [220,126,41,94,7,75,149,174,9,125] Clinical Informatics Storage of EHR [28,15,116,74,135,190,134,227,171,206] Retrieval of EHR [96,241] Interactive data retrieval for Data Sharing [64,14,251,14,44,113,270] Treatment Recommendation [47,43,68,131] Business Transformation [267,268,269,103,266,146] Disease Predication, Diagnosis and Progression [188,40,2,292] Data Security [235,246,163,238] Public [4,143,108,34,38,157] Signal Integration and Aggregation [229,23,291,182] billings, inefficiencies that may result in wasteful diagnostic tests, over-payments due to false claims. Personal data is extremely sensitive due to its profitable value in black-markets, thus, healthcare industry is 200% more likely to experience data breaches than any other. ...
Clinicians decisions are becoming more and more evidence-based meaning in no other field the big data analytics so promising as in healthcare. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate and manage large amount of healthcare data either in structured or unstructured form. In this paper, we would like to address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare i.e.medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We presents different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare.
... Arwa et al in [9] discussed how fog computing is a promising technology for distributed computing providing many services to the edge network, yet faces lots of security and privacy problems in devices. Since IoT devices have limited resources regarding storage, battery, and processing capabilities, fog computing is new service that will provide a new solution for handling these real-time requirements which are not completely achieved by cloud computing [15]. The authors in [9] explained how fog computing could also address many security and privacy issues as in the cloud computing. ...
... • Another cloud-based application was developed to integrate the healthcare data to enable a fast interaction between the patient and healthcare provider (Sultana et al., 2014). ...
Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasised are definition of big data; architectural platforms supporting data analytics; followed by the application of above-mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.
... • Another cloud-based application was developed to integrate the healthcare data to enable a fast interaction between the patient and healthcare provider (Sultana et al., 2014). ...
Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasised are definition of big data; architectural platforms supporting data analytics; followed by the application of above-mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.
... A key component in our architecture is a new searchable encryption (SE) scheme-Posterior Playfair Searchable Encryption (PPSE)-which, besides keeping the same confidentiality levels of the stored data, hides the search patterns to the adversary, bringing improvements when compared to the remaining practical state-of-the-art SE schemes. [44]described the design of an Information Integration and Informatics framework that allows storing, integrating and analyzing healthcare data in the cloud. The Information Integration and Informatics framework allow the development of advanced healthcare application with data integrated in difference database. ...
This work presents a security model in which medical professionals and other users can have access to patient’s health data stored in the cloud without knowledge of the identity of the particular patient which the database belongs to. The access control techniques used in this project grants classifier access to patient data, that is to say, it grants access to data based on the role of the data user. Thus, the aim and objective of the project is achieved.
Clinical decisions are more promising and evidence-based, hence, big data analytics to assist clinical decision-making has been expressed for a variety of clinical fields. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention, and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate, and manage large amount of healthcare data either in structured or unstructured form. In this review, we address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare, i.e., medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We present different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally, the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare.
Personalized Medicine is an important strategy for disease diagnosis and pre-care. This approach considers his/her personal data, circumstances, and genes to cure the disease. This method allows physicians and researchers to prognosticate the medication and prevent policies for appropriate viruses. The concept of cognitive computing works like a human brain to analyze and process the data. This method includes an automated system for using natural language processing, pattern recognition, and data mining to simulate how the human brain works. In this review, the synthesized overview of the current status of research on personalized medicine and how to address personalized medicine, using cognitive computing, are discussed. In addition, the national and international status of the research and issues, regarding personalized medicine, are presented. © 2018, International Association of Computer Science and Information Technology.
