Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future. <s> BIB001 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> This article presents a novel method for diagnosis of valvular heart disease (VHD) based on phonocardiography (PCG) signals. Application of the pattern classification and feature selection and reduction methods in analysing normal and pathological heart sound was investigated. After signal preprocessing using independent component analysis (ICA), 32 features are extracted. Those include carefully selected linear and nonlinear time domain, wavelet and entropy features. By examining different feature selection and feature reduction methods such as principal component analysis (PCA), genetic algorithms (GA), genetic programming (GP) and generalized discriminant analysis (GDA), the four most informative features are extracted. Furthermore, support vector machines (SVM) and neural network classifiers are compared for diagnosis of pathological heart sounds. Three valvular heart diseases are considered: aortic stenosis (AS), mitral stenosis (MS) and mitral regurgitation (MR). An overall accuracy of 99.47% was achieved by proposed algorithm. <s> BIB002 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Increasing myocardial contractility has long been considered a big help for patients with systolic heart failure, conferring an augmented haemodynamic profile in terms of higher cardiac output, lower cardiac filling pressure and better organ perfusion. Though concerns have been raised over the safety issues regarding the clinical trials of different inotropes in hearts with systolic dysfunction, they still stand as a main therapeutic strategy in many centres dealing with such patients. They must be used as short in duration, low in dose and stopped as early as possible. Evidence-based guidelines have provided clinicians with valuable data for better applying inotropes in heart failure patients. In this paper, the authors address clinical trials with different agents used for increasing cardiac contractility in heart failure patients. Furthermore, the authors focus on recent guidelines on making the most out of inotropes in heart failure patients. <s> BIB003 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Infantile and juvenile obesity is a medical, social and cultural problem, which reaches epidemic proportions worldwide. In Romania, according to the World Health Organization, one out of four Romanians is obese. As far as infantile obesity is concerned, Romania ranks third in Europe. Infantile obesity has a major impact on all body systems and is associated with risk factors for cardiovascular diseases and accelerated atherosclerotic processes, including hypertension, atherogenic dyslipidemia, atherosclerosis, metabolic syndrome and functional and structural cardiac changes. Aims: To study the cardiovascular complications of the obesity at children and adolescents. Material and methods: Our study was conducted on a group of 188 obese and overweight patients that were hospitalized in the Pediatric Cardiology Department, the “Sfânta Maria” Children’s Hospital of Iasi between 1 January 2006 and 1 January 2011. The patients’ evaluation was based on: anamnesis, clinical criteria and paraclinical criteria. Results: The distribution of cases by age group was the following: 51% of patients were aged 7-13 years, 42% were teenagers and only 6% of patients were aged between 2-6 years. As far as the patients’ origin is concerned, we noted that 58% of them lived in urban areas. In the study group, 21% of the subjects had borderline hypertension, while 16% of them suffered from definite hypertension. The echocardiogram that was performed on all the patients included in the study revealed hypertrophic cardiomyopathy in 12% of the patients. Conclusions: Child obesity has a major impact on all the big body systems and it is known to contribute to significant increases in the mortality and morbidity rates. <s> BIB004 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Developing holistic predictive modeling solutions for risk prediction is extremely challenging in healthcare informatics. Risk prediction involves integration of clinical factors with socio-demographic factors, health conditions, disease parameters, hospital care quality parameters, and a variety of variables specific to each health care provider making the task increasingly complex. Unsurprisingly, many of such factors need to be extracted independently from different sources, and integrated back to improve the quality of predictive modeling. Such sources are typically voluminous, diverse, and vary significantly over the time. Therefore, distributed and parallel computing tools collectively termed big data have to be developed. In this work, we study big data driven solutions to predict the 30-day risk of readmission for congestive heart failure (CHF) incidents. First, we extract useful factors from National Inpatient Dataset (NIS) and augment it with our patient dataset from Multicare Health System (MHS). Then, we develop scalable data mining models to predict risk of readmission using the integrated dataset. We demonstrate the effectiveness and efficiency of the open-source predictive modeling framework we used, describe the results from various modeling algorithms we tested, and compare the performance against baseline non-distributed, non-parallel, non-integrated small data results previously published to demonstrate comparable accuracy over millions of records. <s> BIB005 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Abstract Catheter ablation is now recognized as one of the potent options to reduce the number of implantable cardioverter defibrillator (ICD) shock therapies by modifying arrhythmic substrate in patients affected by ventricular tachycardia (VT). However, operators often face some difficulties during mapping and ablation procedure of VT; unstable VT makes it difficult to create activation map, the thick ventricular myocardium yields complex tachycardia circuit, and endocardial radiofrequency (RF) energy application hardly creates transmural lesion. Thus some novel approaches are required to improve outcome of VT ablation. Substrate mapping aiming low voltage or abnormal fragmented potential could identify the critical slow conduction area during sinus rhythm and define the target of RF delivery. CT and MRI image integrated to electroanatomical mapping (EAM) system also could help to find diseased myocardium. In this article focused on VT mapping procedure, conventional and novel mapping technique as well as some technical tips should be reviewed comprehensively. <s> BIB006 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Echocardiography (Echo) reports of the patients with pediatric heart disease contain many disease related information, which provide great support to physicians for clinical decision. Such as treatment customization based on the risk level of the specific patient. With the help of natural language processing (NLP), information can be automatically extracted from free-text reports. Those structured data is much easier to analyze with the existing data mining approaches. In this study, we extract the entity/anatomic site-feature-value (EFV) triples in the Echo reports and predict the risk level on this basis. The prediction accuracy of machine learning and rule-based method are compared based on a manual prepared ideal data, to explore the application of automatic knowledge extraction on clinical decision support. <s> BIB007 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <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> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Medical imaging in the era of precision medicine aims for accurate phenotyping of diseases that can benefit from early targeted therapies. We hypothesize that cardiac biomechanics generate high level of information that can leverage big data analytics and machine-learning frameworks for automated <s> BIB009 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Accurate estimation of left ventricle (LV) volumes plays an essential role in clinical diagnosis of cardiac diseases using MRI. Conventional methods of estimating ventricular volumes depend on the results of manual or automatic segmentation of MRI. However, manual segmentation of MRI sequences is extremely time-consuming and subjective, and automatic segmentation is still a challenging task. Therefore, this study aims to develop a new LV volumes prediction method without segmentation, motivated by deep learning technology and the large scale cardiac MRI (CMR) datasets from the second Annual Data Science Bowl (ADSB) in 2016. The experiments results shows that the predicted LV volumes have high correlation with the ground truth. These results prove that the proposed method has big potential to be researched and applied in clinical diagnosis and screening of cardiac diseases. <s> BIB010 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Risk stratification in the context of sudden cardiac death has been acknowledged as one of the major challenges facing cardiology for the past four decades. In recent years, the advent of high performance computing has facilitated organ-level simulation of the heart, meaning we can now examine the causes, mechanisms and impact of cardiac dysfunction in silico. As a result, computational cardiology, largely driven by the Physiome project, now stands at the threshold of clinical utility in regards to risk stratification and treatment of patients at risk of sudden cardiac death. In this white paper, we outline a roadmap of what needs to be done to make this translational step, using the relatively well-developed case of acquired or drug-induced long QT syndrome as an exemplar case. <s> BIB011 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Smartphone-based technologies along with broadband connectivity are changing the way modern cardiology is practiced. The ever broadening connectivity and increasing capabilities of smartphone-based technologies can better monitor, diagnose, and prevent cardiovascular diseases. Researchers can leverage the ubiquitous use of smartphone-based technologies and their constant stream of biometric data to establish large community-based clinical research studies. Patient engagement is enhanced with constant and on-demand access to physicians, daily self-monitoring, and expanding social networks. On the other hand, the exponential growth of smartphone-based technologies invariably disrupts the traditional healthcare model and leaves a vacuum in the infrastructure, medico-legal apparatus, and reimbursement systems that need to be addressed. In this review, we present a comprehensive discussion of the various applications utilizing smartphone-based technologies in cardiology. <s> BIB012 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Big data has attracted much attention from academia and industry. But the discussion of big data is disparate, fragmented and distributed among different outlets. This paper conducts a systematic and extensive review on 186 journal publications about big data from 2011 to 2015 in the Science Citation Index (SCI) and the Social Science Citation Index (SSCI) database aiming to provide scholars and practitioners with a comprehensive overview and big picture about research on big data. The selected papers are grouped into 20 research categories. The contents of the paper(s) in each research category are summarized. Research directions for each category are outlined as well. The results in this study indicate that the selected papers were mainly published between 2013 and 2015 and focus on technological issues regarding big data. Diverse new approaches, methods, frameworks and systems are proposed for data collection, storage, transport, processing and analysis in the selected papers. Possible directions for f... <s> BIB013 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Cardiovascular diseases are the main cause of mortality. Many times healthcare professionals tend to segregate patient populations in different kind of risks: low, intermediate and high depending on standard clinical variables such as age, systolic blood pressure (SBP), smoking and diabetes. Normally one of the most used factors for risk stratification is low density lipoprotein (LDL) as well as Framingham risk score (FRS). On the other hand, using a powerful discriminator as Coronary artery calcium (CAC) it’s possible to separate patients included in a database better than using LDL. The aim of this work was to study the association between clustering of cardiovascular risk factors and the earlier discriminators of cardiovascular disease. <s> BIB014 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> In this paper, we introduce a novel method to define semi-automatically fuzzy partition rules to provide a powerful and accurate insight into cardiac arrhythmia. In particular, we define a text mining approach applied to a large dataset consisting of the freely available scientific papers provided by PubMed. The information extracted is then integrated with expert knowledge, as well as experimental data, to provide a robust, scalable and accurate system, which can successfully address the challenges posed by the management and assessment of big data in the medical sector. The evaluation we carried out shows an accuracy rate of 93% and interpretability of 0.646, which clearly shows that our method provides an excellent balance between accuracy and system transparency. Furthermore, this contributes substantially to the knowledge discovery and offers a powerful tool to facilitate the decision-making process. Copyright © 2015 John Wiley & Sons, Ltd. <s> BIB015 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Abstract Recent advances in imaging technology have enabled the non-invasive study of the structure and the function of the heart, the valves and the vascular system. Different imaging modalities are routinely used to provide specific and complementary diagnostic and prognostic information. Computerized analysis plays a crucial role to in quantifying cardiac function from non-invasive imaging. To this respect, model-based techniques, such as statistical shape models (SSMs), have become a popular solution for the detection of different cardiac structures. In this two-steps approach, a statistical model, trained on a set of samples to encode the morphology and the statistical variability of the structure of interest, is applied to segment the same structure in new images constraining the possible deformations only to plausible shapes observed in the training set. The aim of this chapter is to give a summary of the current state-of-the-art of SSMs in cardiac imaging. In particular, the most relevant and recent SSMs applications proposed for a specific structure (left ventricle, right ventricle, atria and valves) or more cardiac structures together (left and right ventricles, four chambers and entire heart) will be discussed. Furthermore, the potential usefulness of this technique as well as its robustness when applied to different imaging modalities are reviewed. <s> BIB016 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> We are moving into the age of “Big Data” in biomedical research and bioinformatics. This trend could be encapsulated in this simple formula: D = S x F, where the volume of data generated (D) increases in both dimensions: the number of samples (S) and the number of sample features (F). Frequently, a typical bioinformatics problem (e.g. classification) includes redundant and irrelevant features that can result, in the worst-case scenario, in false positive results. Then, Feature Selection (FS) constitutes an enormous challenge. Despite the number and diversity of algorithms available, the proper choice of an approach for facing a specific problem often falls in a “grey zone”. In this study, we select a subset of FS methods to develop an efficient workflow and an R package for bioinformatics machine learning problems. We cover relevant issues concerning FS, ranging from domains problems to algorithm solutions and computational tools. Finally, we use seven different proteomics and gene expression datasets to evaluate the workflow and guide the FS process. <s> BIB017 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> OBJECTIVES ::: An intelligent information technology based system could have a positive impact on the life-style of patients suffering from chronic diseases by providing useful health recommendations. In this paper, we have proposed a hybrid model that provides disease prediction and medical recommendations to cardiac patients. The first part aims at implementing a prediction model, that can identify the disease of a patient and classify it into one of the four output classes i.e., non-cardiac chest pain, silent ischemia, angina, and myocardial infarction. Following the disease prediction, the second part of the model provides general medical recommendations to patients. ::: ::: ::: METHODS ::: The recommendations are generated by assessing the severity of clinical features of patients, estimating the risk associated with clinical features and disease, and calculating the probability of occurrence of disease. The purpose of this model is to build an intelligent and adaptive recommender system for heart disease patients. The experiments for the proposed recommender system are conducted on a clinical data set collected and labelled in consultation with medical experts from a known hospital. ::: ::: ::: RESULTS ::: The performance of the proposed prediction model is evaluated using accuracy and kappa statistics as evaluation measures. The medical recommendations are generated based on information collected from a knowledge base created with the help of physicians. The results of the recommendation model are evaluated using confusion matrix and gives an accuracy of 97.8%. ::: ::: ::: CONCLUSION ::: The proposed system exhibits good prediction and recommendation accuracies and promises to be a useful contribution in the field of e-health and medical informatics. <s> BIB018 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome that may benefit from improved subtyping in order to better characterize its pathophysiology and to develop novel targeted therapies. The United States Precision Medicine Initiative comes amid the rapid growth in quantity and modality of clinical data for HFpEF patients ranging from deep phenotypic to trans-omic data. Tensor factorization, a form of machine learning, allows for the integration of multiple data modalities to derive clinically relevant HFpEF subtypes that may have significant differences in underlying pathophysiology and differential response to therapies. Tensor factorization also allows for better interpretability by supporting dimensionality reduction and identifying latent groups of data for meaningful summarization of both features and disease outcomes. In this narrative review, we analyze the modest literature on the application of tensor factorization to related biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest multiple tensor factorization formulations capable of integrating the deep phenotypic and trans-omic modalities of data for HFpEF, or accounting for interactions between genetic variants at different omic hierarchies. We encourage extensive experimental studies to tackle challenges in applying tensor factorization for precision medicine in HFpEF, including effectively incorporating existing medical knowledge, properly accounting for uncertainty, and efficiently enforcing sparsity for better interpretability. <s> BIB019 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> As medical research techniques and quality have improved, it is apparent that cardiovascular problems could be better resolved by more strict experiment design. In fact, substantial time and resources should be expended to fulfill the requirements of high quality studies. Many worthy ideas and hypotheses were unable to be verified or proven due to ethical or economic limitations. In recent years, new and various applications and uses of databases have received increasing attention. Important information regarding certain issues such as rare cardiovascular diseases, women's heart health, post-marketing analysis of different medications, or a combination of clinical and regional cardiac features could be obtained by the use of rigorous statistical methods. However, there are limitations that exist among all databases. One of the key essentials to creating and correctly addressing this research is through reliable processes of analyzing and interpreting these cardiologic databases. <s> BIB020 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Experimentally-calibrated populations of models (ePoMs) allow to elucidate the trends hidden behind large amounts of data, but they have never been used as a means to monitor the temporal evolution of a heart's electrical activity. This work aimed at using ePoMs to understand the cell membrane anomalies that lead to heart failure. A population of the Tusscher-Noble-Noble-Panfilov model was calibrated to the activation-recovery intervals measured from epicardial electrograms acquired during a Physio-heart experiment. A Mann-Whitney-Wilcoxon U-test was performed on the statistical distributions of the calibrated parameters, at different time points during the experiment, to elucidate the physiology changes that would have led to the resulting ePoMs. The methodology developed in this paper could detect the specific pathological ion dynamics responsible for the abnormal electrical behavior observed during the experiment. Furthermore, the analysis of the electrical activities was capable of detection of pathologies at an earlier stage when compared to the analysis of the cardiac output alone. The use of big data analytics proved to be more effective than the traditional signal analysis approach in predicting heart failure; additionally, this approach accounts for variabilities in both the healthy and the pathological conditions. <s> BIB021 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Next generation health and fitness wearable devices offer the opportunities for improving personalized healthcare. To detect and predict health problems, wearable devices with limited power resource need to transmit sensing data uninterruptedly that could be a huge challenge. Many exist researches use various ways to reduce the energy consumption but, to the best of our knowledge, no one employs physical activities integrated with data transmission technique to save the power. In this paper, we have developed an Adaptive Energy-efficient Data transmission (AED) scheme, which can detect critical events such as myocardial infarction and, at the same time, minimizes data transmission from the devices. Simulation results show that AED reduces the number of transmission by 71.35% for continuous data transmission and 30.33% for batch data transmission. <s> BIB022 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Arrhythmic behaviors are a major risk to the population. These are diverse and can have their origin in cellular dynamics that affect the functioning of the heart. When trying to understand the mechanisms behind arrhythmogenesis the epicardial electrograms present themselves as a useful measurement because they reflect the electrical behavior of the cells surrounding the electrodes. Nevertheless, there is a lack of methods in the literature to automatically process and analyze these signals. In this paper, an algorithm to automatically detect the R, S and T wave peaks in epicardial electrogram signals is presented. This algorithm uses the derivative of the signal to find the activation and recovery times, and uses these as fiducial points to find the desired features. These features are then used as inputs to an artificial neural network, trained to classify individual beats into ‘healthy’ and ‘pathological’. After optimization, both the detector and the neural network showed good performance in their tasks; furthermore, the robustness and amenability to real-time implementation of the methods here presented make them ideal for monitoring patients or experimental platforms when epicardial electrograms can be measured. <s> BIB023 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <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> BIB024 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Abstract A cloud-based analysis system of cardiac images is constructed to realize the remote sharing of cardiac images and related computing services. In its core service, a novel image post-processing approach is presented to obtain information on individual in vivo myocardial fibers. This approach is based on the anisotropic decomposition of the local deformation using tagged magnetic resonance (tMR) images of the heart. The local sine wave model (SinMod) is used in our approach to trace the motion in image sequences. Within this motion framework, a pair of anisotropic deformation components for each pixel is then extracted by Poisson orthogonal composition, which can represent exactly the local Poisson Effect. Based on these pair components, the direction structure (by the major deformation vector field) and the elastic property (by Poisson ratio) of myocardial fiber can be estimated. The experimental results demonstrate that our proposed approach is useful in analyzing information of in vivo myocardial fibers. It will have potential to provide the valuable cloud computing service for remote cardiac diagnosis and treatment. <s> BIB025 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> INTRODUCTION ::: Medication adherence is of key importance in the treatment of cardiovascular disease. Studies consistently show that a substantial proportion of patients is non-adherent. Areas covered: For this review, telemedicine solutions that can potentially improve medication adherence in patients with cardiovascular disease were reviewed. A total of 475 PubMed papers were reviewed, of which 74 were assessed. Expert commentary: Papers showed that evidence regarding telemedicine solutions is mostly conflictive. Simple SMS reminders might work for patients who do not take their medication because of forgetfulness. Educational interventions and coaching interventions, primarily delivered by telephone or via a web-based platform can be effective tools to enhance medication adherence. Finally, it should be noted that current developments in software engineering may dramatically change the way non-adherence is addressed in the nearby future. <s> BIB026 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> BACKGROUND The explosive increase in medical literature has changed therapeutic strategies, but it is challenging for physicians to keep up-to-date on the medical literature. Scientific literature data mining on a large-scale of can be used to refresh physician knowledge and better improve the quality of disease treatment. MATERIAL AND METHODS This paper reports on a reformulated version of a data mining method called MedRank, which is a network-based algorithm that ranks therapy for a target disease based on the MEDLINE literature database. MedRank algorithm input for this study was a clear definition of the disease model; the algorithm output was the accurate recommendation of antihypertensive drugs. Hypertension with diabetes mellitus was chosen as the input disease model. The ranking output of antihypertensive drugs are based on the Joint National Committee (JNC) guidelines, one through eight, and the publication dates, ≤1977, ≤1980, ≤1984, ≤1988, ≤1993, ≤1997, ≤2003, and ≤2013. The McNemar's test was used to evaluate the efficacy of MedRank based on specific JNC guidelines. RESULTS The ranking order of antihypertensive drugs changed with the date of the published literature, and the MedRank algorithm drug recommendations had excellent consistency with the JNC guidelines in 2013 (P=1.00 from McNemar's test, Kappa=0.78, P=1.00). Moreover, the Kappa index increased over time. Sensitivity was better than specificity for MedRank; in addition, sensitivity was maintained at a high level, and specificity increased from 1997 to 2013. CONCLUSIONS The use of MedRank in ranking medical literature on hypertension with diabetes mellitus in our study suggests possible application in clinical practice; it is a potential method for supporting antihypertensive drug-prescription decisions. <s> BIB027 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> The field of cardiology has long used wearable medical devices to monitor heart rate and rhythm. The past decade has seen the emergence of many new wearable devices, including several that have been widely adopted by both physicians and consumers. In this review, we discuss existing and forthcoming devices designed to measure activity, heart rate, heart rhythm, and thoracic fluid. We also offer several frameworks to classify and better understand wearable devices, such that we may weigh their potential benefit in improving healthcare with the many challenges that must be addressed to reap these benefits. <s> BIB028 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> The biomedical module of the paper consists of a Photoplethysmographic (PPG) signal and an ECG sensor. Due to the change of the amplitude of the optical sensor, the PPG sensor can sense the signal components with less power supply noise and electromagnetic interference. First of all, we observe the signal through the digital signal processor (DSP), then calculate the heart rate, blood oxygen saturation (SPO2) and blood pressure. The ESP8266 Wi-Fi module sends signal messages to the backend for classification. In this paper, we use Support Vector Machine(SVM) to classify these three feature vectors. The classification results will show the data into healthy, unhealthy, very unhealthy, and explore the accuracy of classification prediction by using SVM. Finally, the physiological parameters of the test object and classification result uploaded to the cloud storage and webpage display in order to provide the basis for big data analysis in future research. <s> BIB029 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Despite effective medications, rates of uncontrolled hypertension remain high. Treatment protocols are largely based on randomized trials and meta-analyses of these studies. The objective of this study was to test the utility of machine learning of big data in gaining insight into the treatment of hypertension. We applied machine learning techniques such as decision trees and neural networks, to identify determinants that contribute to the success of hypertension drug treatment on a large set of patients. We also identified concomitant drugs not considered to have antihypertensive activity, which may contribute to lowering blood pressure (BP) control. Higher initial BP predicts lower success rates. Among the medication options and their combinations, treatment with beta blockers appears to be more commonly effective, which is not reflected in contemporary guidelines. Among numerous concomitant drugs taken by hypertensive patients, proton pump inhibitors (PPIs), and HMG CO-A reductase inhibitors (statins) significantly improved the success rate of hypertension. In conclusions, machine learning of big data is a novel method to identify effective antihypertensive therapy and for repurposing medications already on the market for new indications. Our results related to beta blockers, stemming from machine learning of a large and diverse set of big data, in contrast to the much narrower criteria for randomized clinic trials (RCTs), should be corroborated and affirmed by other methods, as they hold potential promise for an old class of drugs which may be presently underutilized. These previously unrecognized effects of PPIs and statins have been very recently identified as effective in lowering BP in preliminary clinical observations, lending credibility to our big data results. <s> BIB030 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Background ::: Machine learning (ML) represents a family of algorithms that has rapidly developed within the last years in a wide variety of knowledge areas. ML is able to elucidate and grasp complex patterns from data in order to approach prediction and classification problems. The present narrative review summarizes fundamental notions in ML as well as the evidence of its application in standard cardiac imaging and the potential for implementation in cardiac hybrid imaging. <s> BIB031 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> AIMS: Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research. METHODS AND RESULTS: We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health. CONCLUSION: High volumes of inherently diverse ('big') EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare. <s> BIB032 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> RATIONALE, AIMS, AND OBJECTIVE ::: The aim of this study was to explore the views of prescribers in cardiology in Sudan about the use of guidelines in clinical practice and the extent to which guidelines whether national or international can be adopted in clinical practice in Sudan. ::: ::: ::: METHODS ::: Interviews were conducted with the consultants in 2 of the main cardiac hospitals in Sudan. This was followed by a survey amongst the doctors in the hospitals to examine the views of a larger population of prescribers about the matter investigated. ::: ::: ::: RESULTS ::: Twelve consultants were interviewed, and 47 prescribers (60%) replied to the questionnaire that followed. Most doctors relied on foreign guidelines to prescribe for their patients. The doctors acknowledged the limitation of using foreign guidelines in Sudan. A number of doctors were not in favour of following any guidelines, as they perceived that the practice in Sudan does not allow implementation of guidelines. ::: ::: ::: CONCLUSION ::: The prescribers in Sudan had to rely on guidelines made in foreign countries if they want to get the benefit of evidence-based medicine to their patients, but they had to find a way to adapt these guidelines to their patients and to the health care system they are working within. However, it is not known if this adaptation of foreign guidelines is providing the benefits intended or is risking evidence-based medicine. <s> BIB033 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> We empirically investigate the pattern of where heart attack patients are transferred between hospitals. Using 2011 Florida State Emergency Department and Inpatient Databases, we demonstrate the relative importance of three key factors in determining transfer destinations: (1) the distance between sending and receiving hospitals, (2) publicly reported quality measures of receiving hospitals, and (3) the relationship between sending and receiving hospitals as indicated by whether they are affiliated with the same multihospital system. Our conditional logit analysis shows that hospital relationship plays a more dominant role in determining transfer destinations than distance and quality. This result is robust to three alternative specifications of choice sets using distance ranking, radius circles, and Hospital Referral Regions, and also robust to alternative measures of distance and quality. Using 30‐day readmission rate to evaluate the health outcome of transferred patients, we find that relationship‐based transfers led to worse outcome than distance‐based and quality‐based transfers. We also find that nonprofit hospitals are more likely to conduct quality‐based transfers and less likely to conduct relationship‐based transfers than their for‐profit counterparts. Our study calls for reevaluating the current practice of relationship‐driven routing of heart attack patients—selecting transfer destinations based on quality or distance can potentially decrease hospital readmission rate. <s> BIB034 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> A. CONCEPT AND DEFINITION OF MEDICAL BIG DATA <s> Data Mining in general is the act of extracting interesting patterns and discovering non-trivial knowledge from a large amount of data. Medical data mining can be used to understand the events happened in the past, i.e. studying a patients vital signs to understand his complications and discover why he has died, or to predict the future by analyzing the events that had happened. In this chapter we are presenting an overview on studies that use data mining to predict heart failure and heart diseases classes. We will also focus on one of the trendiest data-mining field, namely the Sequential Mining, which is a very promising paradigm. Due to its important results in many fields, this chapter will also cover all its extensions from Sequential Pattern Mining, to Sequential Rule Mining and Sequence Prediction. Pattern Mining is the discovery of important and unexpected patterns or information and was introduced in 1990 with the well-known Apriori. Sequential Patterns Mining aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. The importance of a sequence can be measured based on different factors such as the frequency of their occurrence, their length and their profit. In 1995, Agrawal et al. introduced a new Apriori algorithm supporting time constraints named AprioriAll. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Time dimension is a very important factor in analyzing medical data, making it necessary to present a positioning of Sequential Mining in the medical domain. <s> BIB035
Healthcare has no specific meaning of ''big data.'' Big data is commonly categorized into 5 Vs. Figure 10 shows the 5 Vs of the big data. Since, the last decade there have been a fast growing digitalization in the field of medical and information is increases rapidly with the wide spread use of internet and global communication. Healthcare system is facing the transformation of information with the enhancement in healthcare information system, electronic medical record, wearable, handheld, and smart devices. As a result, a huge bulk of data is produced from these digital transformations. This prompt increase in medical big data and development of computation in the field healthcare enable researchers and practitioners to extract and visualise the new insights from this medical big data. The idea behind big data analytics came into existence by the help of which we can analyze complex data and derive valuable facts from it. When these analytics apply to the medical data, then it has massive potential for refining the care quality, reduce the care cost, and reducing waste and error. McKinsey Global Institute BIB001 presents, if the data is utilized efficiently, then the US health care can make an addition than $300 billion every year, of which 2/3 would be reducing expenses by about 8% in the healthcare. Different resources of medical big data are available, such as the data of biometrics, registration, electronic health records, patient reports, internet, imaging, the data of biomarkers, prospective cohort studies, clinical, and the data of administration BIB008 . The complication of healthcare results from variety of healthcare associated devices and their co-morbidities, the heterogeneity of treatments and outcomes, and the subtle intricacies of study development, investigative method and approach to collect, process, and interpret healthcare data BIB024 . Table 4 shows the volume of big data in the present research. Table 5 shows the Variety, value, veracity, velocity of big data in the proposed research. The following are the journals which have published the research related to the proposed research. Till now, researchers and practitioners try to come across a complete study of the healthcare big data in the area of cardiology and related disease. Several studies in the literature exist about the heart disease, but yet, there is lack of detailed study to present the prominent heart issues. The proposed study is an attempt to cover this gap and presented the work done in the healthcare big data in the heart related issues. Table 7 shows the representation of the work done in the area of medical big data in heart related issues since January 2008 to August 2018. The details of the papers  ,  ,  , BIB025 , BIB026 - BIB027 , BIB035 , BIB028 , BIB029 , BIB030 , BIB031 , BIB032 - BIB033 ,  ,  ,  , BIB016 , BIB017 , BIB018 ,  , BIB019 , BIB034 , BIB020 - BIB021 ,  , BIB022 ,  , BIB009 ,  , BIB010 , BIB011 , BIB012 , BIB013 , BIB014 , BIB015 ,  , BIB007 ,  ,  , BIB006 , BIB004 , BIB002 , BIB003 are already given in Details of the papers  ,  ,  , BIB026 , BIB027 , BIB035 , BIB028 , BIB030 , BIB032 ,  ,  , BIB018 , BIB021 ,  , BIB022 ,  ,  , BIB011 , BIB014 , BIB015 ,  ,  , BIB005 are already given in Table 6 while Table 8 show the representation of rest of the papers in the field of medical big data features in cardiology. The paper  ,  ,  , BIB027 , BIB035 , BIB032 ,  , BIB023 ,  , BIB022 ,  , BIB009 ,  , BIB015 , BIB007 ,  , BIB005 are already discussed in table 6, while the table 9 shows the representation of rest of the paper for the applications of healthcare big data in cardiology.