Big data, artificial intelligence, and cardiovascular precision medicine

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

Artificial Intelligence and Cardiovascular Genetics

Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.

Artificial Intelligence-Powered Blockchains for Cardiovascular Medicine

Clinical databases, particularly those composed of big data, face growing security challenges. Blockchain, the open, decentralized, distributed public ledger technology powering cryptocurrency, records transactions securely without the need for third-party verification. In the health care setting, decentralized blockchain networks offer a secure interoperable gateway for clinical research and practice data. Here, we discuss recent advances and potential future directions for the application of blockchain and its integration with artificial intelligence (AI) in cardiovascular medicine. We first review the basic underlying concepts of this technology and contextualise it within the spectrum of current, well known applications. We then consider specific applications for cardiovascular medicine and research in areas such as high-throughput gene sequencing, wearable technologies, and clinical trials. We then evaluate current challenges to effective implementation and future directions. We also summarise the health care applications that can be realised by combining decentralized blockchain computing platforms (for data security) and AI computing (for data analytics). By leveraging high-performance computing and AI capable of securely managing large and rapidly expanding medical databases, blockchain incorporation can provide clinically meaningful predictions, help advance research methodology (eg, via robust AI-blockchain decentralized clinical trials), and provide virtual tools in clinical practice (eg, telehealth, sensory-based technologies, wearable medical devices). Integrating AI and blockchain approaches synergistically amplifies the strengths of both technologies to create novel solutions to serve the objective of providing precision cardiovascular medicine.

Cardioinformatics: the nexus of bioinformatics and precision cardiology

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

Deep learning for cardiovascular medicine: a practical primer

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the 'black-box' criticism), its need for extensive adjudicated ('labelled') data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.

Hyponatremia in Heart Failure: Pathogenesis and Management

Hyponatremia is a very common electrolyte abnormality, associated with poor short- and long-term outcomes in patients with heart failure (HF). Two opposite processes can result in hyponatremia in this setting: Volume overload with dilutional hypervolemic hyponatremia from congestion, and hypovolemic hyponatremia from excessive use of natriuretics. These two conditions require different therapeutic approaches. While sodium in the form of normal saline can be lifesaving in the second case, the same treatment would exacerbate hyponatremia in the first case. Hypervolemic hyponatremia in HF patients is multifactorial and occurs mainly due to the persistent release of arginine vasopressin (AVP) in the setting of ineffective renal perfusion secondary to low cardiac output. Fluid restriction and loop diuretics remain mainstay treatments for hypervolemic/dilutional hyponatremia in patients with HF. In recent years, a few strategies, such as AVP antagonists (Tolvaptan, Conivaptan, and Lixivaptan), and hypertonic saline in addition to loop diuretics, have been proposed as potentially promising treatment options for this condition. This review aimed to summarize the current literature on pathogenesis and management of hyponatremia in patients with HF.