Data integration of heterogeneous big data sources use case diagram 

Data integration of heterogeneous big data sources use case diagram

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Background: Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computi...

... AURORA clearly meets the various definitions of healthcare big data across the five Vs: volume, variety, velocity, veracity, and value. We also meet other common big data definitions of energy and life-span [8], types of healthcare data [9], and analytic challenges [8,[10][11][12][13]. AURORA encompasses many of the data sources [7,14] typically referenced as part of healthcare big data: (1) clinical and medical (electronic medical records, diagnostic, prescription, brain imaging, functional magnetic resonance imaging, ancillary); (2) patient-generated (phenotypic, survey, audio recordings); (3) sensor and technology platforms (Verily Study Watch TM , digital phenotyping whereby participants use their own smartphones via Mindstrong Discovery APP TM , neurocognitive assessments via TestMyBrain TM web-based technology); and (4) genomic (DNA, RNA, and plasma via blood specimens; saliva). ...

  • Charles E. Knott
  • Stephen Gomori
  • Mai Ngyuen
  • Susan Pedrazzani
  • Kim Chantala

Combining survey data with alternative data sources (e.g., wearable technology, apps, physiological, ecological monitoring, genomic, neurocognitive assessments, brain imaging, and psychophysical data) to paint a complete biobehavioral picture of trauma patients comes with many complex system challenges and solutions. Starting in emergency departments and incorporating these diverse, broad, and separate data streams presents technical, operational, and logistical challenges but allows for a greater scientific understanding of the long-term effects of trauma. Our manuscript describes incorporating and prospectively linking these multi-dimensional big data elements into a clinical, observational study at US emergency departments with the goal to understand, prevent, and predict adverse posttraumatic neuropsychiatric sequelae (APNS) that affects over 40 million Americans annually. We outline key data-driven system challenges and solutions and investigate eligibility considerations, compliance, and response rate outcomes incorporating these diverse "big data" measures using integrated data-driven cross-discipline system architecture.

... We created a testable narrative use case for COVID-19 surveillance, using previously described methods [25,26]. The primary actor was the RCGP RSC, the system it interacts with was the national response to the COVID-19 pandemic, and its outcomes entailed monitoring spread and effect of mitigation measures. ...

BACKGROUND Creating an ontology for coronavirus disease 2019 (COVID-19) surveillance should help ensure transparency and consistency. Ontologies formalise conceptualisations at either domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform. OBJECTIVE To develop an application ontology for COVID-19 which can be deployed across the various use case domains of the Oxford- RCGP RSC research and surveillance activities. METHODS We described our domain-specific use case. The actor was the RCGP RSC sentinel network; the system the course of the COVID-19 pandemic; the outcomes the spread and effect of mitigation measures. We used our established three-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association (IMIA) Primary Health Care Informatics working group and extended networks. RESULTS Our use case domains included primary care, public health, virology, clinical research and clinical informatics. Our ontology supported: (1) Case identification, microbiological sampling and health outcomes at both an individual practice and national level; (2) Feedback through a dashboard; (3) A national observatory, (4) Regular updates for Public Health England, and (5) Transformation of the sentinel network to be a trial platform. We have identified a total of 8,627 people with a definite COVID-19 status, 4,240 with probable, and 59,147 people with possible COVID-19, within the RCGP RSC network (N=5,056,075). CONCLUSIONS The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.

... The IMIA PCIWG has conducted a series of Delphi groups to research the ethical dimension of informatics and data curation initiatives in primary care since 2011 [9][10][11][12]. Our 2016 Privacy, Ethics, and Data Access Framework for Real World EHR Data was the starting point for this work. ...

Objective: To create practical recommendations for the curation of routinely collected health data and artificial intelligence (AI) in primary care with a focus on ensuring their ethical use. Methods: We defined data curation as the process of management of data throughout its lifecycle to ensure it can be used into the future. We used a literature review and Delphi exercises to capture insights from the Primary Care Informatics Working Group (PCIWG) of the International Medical Informatics Association (IMIA). Results: We created six recommendations: (1) Ensure consent and formal process to govern access and sharing throughout the data life cycle; (2) Sustainable data creation/collection requires trust and permission; (3) Pay attention to Extract-Transform-Load (ETL) processes as they may have unrecognised risks; (4) Integrate data governance and data quality management to support clinical practice in integrated care systems; (5) Recognise the need for new processes to address the ethical issues arising from AI in primary care; (6) Apply an ethical framework mapped to the data life cycle, including an assessment of data quality to achieve effective data curation. Conclusions: The ethical use of data needs to be integrated within the curation process, hence running throughout the data lifecycle. Current information systems may not fully detect the risks associated with ETL and AI; they need careful scrutiny. With distributed integrated care systems where data are often used remote from documentation, harmonised data quality assessment, management, and governance is important. These recommendations should help maintain trust and connectedness in contemporary information systems and planned developments.

...  that cannot be analyzed by old software due to the complexity and high volume [2,9,10]. ...

The rapid development of technology over the past 20 years has led to explosive data growth in various industries, including defense industries, healthcare. The analysis of generated Big Data has recently been addressed by many researchers, because today's Big Data analysis are one of the most important and most profitable areas of development in Data Science and companies that are able to extract valuable knowl edge among the massive amount of data at logical time can earn significant advantages . Accordingly, in this survey, we investigate definition of the Big Data and the data sources. Also look at advantages, challenges, applications, analysis and platforms used in the Big Data.

...  that cannot be analyzed by old software due to the complexity and high volume [2,9,10]. ...

The rapid development of technology over the past 20 years has led to explosive data growth in various industries, including defense industries, healthcare. The analysis of generated Big Data has recently been addressed by many researchers, because today's Big Data analysis are one of the most important and most profitable areas of development in Data Science and companies that are able to extract valuable knowledge among the massive amount of data at logical time can earn significant advantages . Accordingly, in this survey, we investigate definition of the Big Data and the data sources. Also look at advantages, challenges, applications, analysis and platforms used in the Big Data.

... A study found that, if one stops to read each term of agreement in a year, one would waste approximately 76 work days reading them (McDonald and Cranor 2008). When an individual is sharing his data, it is relevant to know the ethical principles of the institution in charge of the data gathering, what they intend to do with the information and what is out of boundaries (Davis 2012;Liyanage et al. 2014). In recent years, we have seen many cases in which data was secretly collected and analyzed, and with no purpose known to the users of the service (van der Sloot 2015). ...

Big data and machine learning are gaining traction in health sciences research. They might provide predictive models for both clinical practice and public health systems. Big data is a broad term used to denote volumes of large and complex measurements. Beyond genomics and other "omic" fields, big data includes administrative, molecular, clinical, environmental, sociodemographic, and even social media information. Machine learning, also known as pattern recognition, represents a range of techniques used to analyze big data by identifying patterns of interaction among features. Compared with traditional statistical methods that provide primarily average group-level results, machine learning algorithms allow predictions and stratification of clinical outcomes at the level of an individual subject. In the present chapter, we provide a concise historical perspective of some important events in health sciences and the analytical methods used to find causes and treatment of illnesses. The overall aim is to understand why big data and machine learning have recently become promising methods to define, predict, and treat illnesses, and how they can transform the way we conceptualize care in health sciences.

... Big Data Analytics (BDA) is the way toward removing learning from sets of Big Data [1]. BDA explore has turned into an imperative imaginative subject crosswise over a wide range of orders [2,3]. The existence sciences and biomedical informatics have been among the most dynamic fields in leading Big Data explore [4]. ...

  • M. S. Roobini M. S. Roobini
  • M. Lakshmi

Big Data (BDA) is progressively turning into a slanting practice that numerous associations are receiving with the motivation behind developing important data from Big Data. The term Big Data is likewise used to catch the openings and difficulties confronting all scientists in overseeing, examining, and incorporating datasets of differing information compose. In this paper we mention how the healthcare factor become more advance in modern world. This includes that the health care data should be properly analyzed so that we can deduce that in which group or gender, diseases attack the most. This beneficial outputs which include: getting the health care analysis in various forms. Thus this concept of analytics should be implemented with a view of future use. Beyond improving profits and cutting down on wasted overhead, Big Data in healthcare is being used to predict epidemics, cure disease, improve quality of life and avoid preventable deaths. With the world's population increasing and everyone living longer, models of treatment delivery are rapidly changing, and many of the decisions behind those changes are being driven by data. The drive now is to understand as much about a patient as possible, as early in their life as possible hopefully picking up warning signs of serious illness at an early enough stage that treatment is far more simple (and less expensive) than if it had not been spotted until later.

... A study found that, if one stops to read each term of agreement in a year, one would waste approximately 76 work days reading them (McDonald and Cranor 2008). When an individual is sharing his data, it is relevant to know the ethical principles of the institution in charge of the data gathering, what they intend to do with the information and what is out of boundaries (Davis 2012;Liyanage et al. 2014). In recent years, we have seen many cases in which data was secretly collected and analyzed, and with no purpose known to the users of the service (van der Sloot 2015). ...

  • Diego Librenza-Garcia Diego Librenza-Garcia

Data science is reshaping our world in ways we never experienced before. This transformation carries an enormous potential to improve mental health care and patient assessment. However, it is not only data gathering that is increasing at a high velocity, but also relevant ethical issues derived from its ownership, analysis, and impact in our lives. In this chapter, we review potential applications of big data analytics and associated dilemmas that may arise from it. We start by discussing issues linked to data itself, involving ownership, privacy, transparency, and reliability. Then, we proceed to discuss what may happen following data processing, and the implementation of predictive models = in real scenarios, focusing on the implications for clinicians, scientists, and patients. We highlight that while it is necessary to develop more strict regulations for handling sensitive data, we must also pay attention to the problem of overregulation, which could create unnecessary obstacles for data science and slow down the potential benefits it may have in our society.

... Big Data Analytics (BDA) is a modern knowledge extraction process from Big Data [16]. BDA has become a featured research topic across many disciplines [9], [17], [19], [20], [21] where medical healthcare and biomedical informatics is the most active field in this research area [10]. The world's most voluminous accumulation of human genetic information made by U. S. National Institute of Health (N IH) is openly available on Amazon cloud computing domain [23]. ...

... For defining Big Data in healthcare, Auffray et al. [26] focused on the types of healthcare data, while authors like Raghupathi & Raghupathi [10], Karen et al. [28], Tan et al. [29] emphasized on the requirement of analytical and management tools. According to Liyanage et al. [30], a quantitative definition of Big Data is difficult because the volume aspect of Big Data is relative to the time of definition and would change with the advancement in technologies. Hansen et al. [25] and Roski et al. [31] concentrate on its analytical ability. ...

... Bates et al. [18] By big data, we refer to the high volume, variety, and potential for the rapid accumulation of data Dinov [25] Big healthcare data refers to complex datasets that have some unique characteristics, beyond their large size, that both facilitate and convolute the process of extraction of actionable knowledge about an observable phenomenon Karen et al. [29] Big Data is a term used to describe data sets with such large volume or complexity that conventional data processing methods are not good enough to deal with them. Tan et al. [30] Big data has been referred to as data that are too complex and large that cannot be processed and managed by traditional data processing tools Hansen et al. [25] In addition to just having more data, Big Data also generally refers to the application of machine learning for analyzing the data sets. Roski et al. [31] Big data-that is, the sophisticated and rapid analysis of massive amounts of diverse Information Bian et al. [32] Big data is commonly defined through the 4 Vs: volume (scale or quantity of data), velocity (speed and analysis of real-time or near-real-time data), variety (different forms of data, often from disparate data sources), and veracity (quality assurance of the data). ...

Background: The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. Purpose: This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. Data sources: A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. Study selection: Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. Data extraction: Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. Results: A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. Conclusion: This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.