Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> Background: In 2004 the concept of evidence-based software engineering (EBSE) was introduced at the ICSE04 conference. Aims: This study assesses the impact of systematic literature reviews (SLRs) which are the recommended EBSE method for aggregating evidence. Method: We used the standard systematic literature review method employing a manual search of 10 journals and 4 conference proceedings. Results: Of 20 relevant studies, eight addressed research trends rather than technique evaluation. Seven SLRs addressed cost estimation. The quality of SLRs was fair with only three scoring less than 2 out of 4. Conclusions: Currently, the topic areas covered by SLRs are limited. European researchers, particularly those at the Simula Laboratory appear to be the leading exponents of systematic literature reviews. The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners. <s> BIB001 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> The authors consider acute problems in the quality and management of medical services challenging health care systems worldwide. This actuality has motivated the representatives of the European Association for Predictive, Preventive and Personalised Medicine and European Federation of Clinical Chemistry and Laboratory Medicine to consider the efforts in promoting an integrative approach based on multidisciplinary expertise to advance health care. The current paper provides a global overview of the problems related to medical services: pandemic scenario in the progression of common chronic diseases, delayed interventional approaches of reactive medicine, poor economy of health care systems, lack of specialised educational programmes, problematic ethical aspects of treatments as well as inadequate communication among professional groups and policymakers. Further, in the form of individual paragraphs, the article presents a consolidated position of the represented European organisations. This position is focused on the patients' needs, expert recommendations for the relevant medical fields and plausible solutions which have a potential to advance health care services if the long-term strategies were to be effectively implemented as proposed here. <s> BIB002 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations. <s> BIB003 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data. <s> BIB004 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> Background: In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality. Objective: The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm. Methods: We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different ::: combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest ::: from the past 10 years. Results: This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type ::: of data. Conclusions: Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to ::: collect and store data. [JMIR Med Inform 2014;2(1):e1] <s> BIB005 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> Over the next 10–20 years, a pessimistic prognosis considers pandemic scenario for type 2 diabetes mellitus, neurodegenerative disorders and some types of cancer followed by the economic disaster of healthcare systems in a global scale. Well-recognised deficits of currently provided medical services result from the delayed ‘disease care’. Herewith EPMA releases the long-term strategies for the effective promotion of predictive, preventive and personalised medicine (PPPM) considered as the medicine of the future. Under the EPMA-umbrella, an international forum of currently 45 countries is actively contributing to the development and implementation of the innovative PPPM concepts. EPMA is open for collaboration with other leading European and global professional networks relevant for the effective promotion of PPPM in sciences and practical implementation. <s> BIB006 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> The exponential increase in Big Data generation combined with limited capitalization on the wealth of information embedded within Big Data has prompted us to revisit our scientific discovery paradigms. A successful transition into this digital era of medicine holds great promise for advancing fundamental knowledge in biology, innovating human health, and driving personalized medicine; however, this will require a drastic shift of research culture in how we conceptualize science and use data. An e-transformation will require global adoption and synergism among computational science, biomedical research, and clinical domains. ::: ::: A scarce number of scientific investigations have innovated clinical diagnosis, prognosis, and therapeutics, despite decades of research and enormities of National Institutes of Health (NIH)–funded research dollars.1,2 This situation requires a global reassessment of whether linear thought processes and reductionistic approaches alone can describe biological processes in a way that translates to valuable information on human systems. Information gleaned from population science using large Big Data data sets has perpetuated a shift in the paradigm of how we define and investigate health and disease in the individual patient.3 We are recognizing the profound value in unorthodox data types and in the integration of diverse data to describe individuals to sufficient depths for discerning clinical outcomes. Biomedicine, along with other fields, has been awakened and awed by the digital wave of major corporations such as Google and Amazon, who have revolutionized the Internet roadmap through developing and refining sophisticated data analytics platforms to accurately describe individual human behavior.4 The reality in biomedical science is that there are zettabytes of high-quality data sitting idly on servers and in cloud infrastructures, and an abundance of biomedical knowledge lies hidden within, yet only a small fraction of this wealth has been harvested. There is an immediate need for data science to … <s> BIB007 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system. <s> BIB008 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> Background ::: It is uncertain whether repeated measurements of a multi-target biomarker panel may help to personalize medical heart failure (HF) therapy to improve outcome in chronic HF. <s> BIB009 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> I. INTRODUCTION <s> Biobanks have during last two decades gained an important role in the whole process of biomedical research and health care not only in Europe but also worldwide. Biobanks are one of the pillars in personalised medicine tackling all its aspects such as prevention, diagnosis, treatment and monitoring closely the specific characteristics of an individual patient. The current and future power of biobanks is the amount of samples of high-quality and related information available for current and future research of diseases, for optimising patients´ prevention, diagnosis, treatment and monitoring. The material stored in biobanks is a treasure for future technologies that will be able to utilise the currently uncovered information and knowledge. A great and growing number of samples and related information also opens new ways in how to tackle the big data problems and population studies. Biobanks play a substantial role in drug discovery, development and validation. Biobanks are not only an issue of biomedical research, but are becoming a public issue involving patients, to actively participate in biobanking with respect to ethical, legal and social issues. And, finally, biobanking as a multidisciplinary and modern field of science requires appropriate education at all levels of society. To be a world leader in the field of biobanking requires wide international and interdisciplinary collaboration. The topic-dedicated programmes released by the European Commission sustainably support biobank development in Europe and the main tool is the biggest European Union (EU) research and innovation programme ever—Horizon 2020. This article reviews the main Horizon 2020 biobanking projects, financing schemes and the future perspectives. <s> BIB010
Systematic literature review (SLR) is a systematic way of identifying, organizing, and representing the literature in a systematic manner BIB001 . The proposed research work is based on the protocol of SLR. In today digital and computing world the information increases rapidly. Devices such as remote sensor and wearable technologies continuously produce abundant heterogeneous data. This data is called big data. The big data is the data whose diversity, scale, and complexity need new structure, algorithm, technique and analytics for the management, visualization and to pull-out The associate editor coordinating the review of this manuscript and approving it for publication was Vlad Diaconita. hidden information BIB003 . Big data has produced key attention in different of fields, such as in social media, banking operations, production of healthcare appliance, and satellite imaging BIB004 . The data is stored in a vastly organized layout to visualize contents and information. Healthcare system is facing the digital transformation with the enhancement in the utilization of healthcare medical information system, electronic medical record, wearable, handheld, and smart devices. A bulk of data is produced from these digital transformations. Recent prompt increase in medical big data and development of computation in the field of cardiology enable researchers and practitioners to extract and visualise the new insights from healthcare big data. Several big data sources in healthcare are available, such as biometric data, registration data, health record electronically, the internet data, imaging, patient reports, biomarker data, clinical data, and administrative data BIB008 . Davarzani et al. BIB009 did analysis on 499 elderly patients with congestive heart failure of age ≥ 60 years who had frequently visited clinic for 19 months to follow-up. Relationship between frequently measurements of biomarkers and treatment effects of loop diuretics, β-blockers, spironolactone, and renin-angiotensin system inhibitors on risk of HF hospitalization was examined in the generation of hypothesis. The equation of generalized estimating was used to report the correlation of patient recurrences of events. Kinkorová and Topolčan BIB010 descibed the key Horizon 2020 of biobanking projects, financing schemes and the future perspective. The EPMA announced the enduring strategies for the useful endorsement of predictive, preventive, and personalized (PPP) medicine. Over 45 countries is contributing toward the implementation and improvement of the promotion of PPP medicine BIB006 . Golubnitschaja et al.  , BIB002 give an overview of the issues associated with healthcare services of pandemic scenario in the progression of frequent non-communicable diseases, poor economy of health care, lackness of specialised education, overdue interventional approaches of reactive medicine, challenging ethical characteristics of some treatments along with insufficient communications among policymakers and professional groups. The paper further presents a combined status of the professionals of PPPM toward the new lifelong instruments for the progress of technology and science in health related medical services in the European programme 'Horizon 2020'. Medical big data is different from the other discipline big data due to its distinctive features. The medical big data is mostly difficult to use and most of the examiners in the healthcare arena are hesitant to exercise open data science for the reasons, such as lack of data-sharing incentives and risk of data exploitation by illegal use BIB007 . Medical big data are normally be collected based on some protocol and are mostly structured BIB005 . The cost of medical big data is normally more, as it involves expensive instrumentations, personnel and discomfort of the patient. Healthcare data are comparatively less in size to the other disciplines. Medical big data has several applications in healthcare, such public health, disease and safety surveillance, predictive modelling and clinical decision support, and many other research areas. The role of medical data in cardiology is a challenging task. Early decision making in cardiology has enormous potential for refining the care, reduce the cost of care, and reducing waste and error. Previously some research work has been done to promote conscious efforts made through the process and execution of healthcare data, but yet no detailed study of healthcare big data in cardiology for the purpose of feature identification, applications, and analytics of medical big data.