Download or read book Big Data in Cardiology Predicting Preventing and Managing Diseases written by Bikal Dhungel and published by GRIN Verlag. This book was released on 2020-10-28 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2020 in the subject Health Sciences - Health Logistics, grade: 1,7, Linnaeus University (School of Informatics), course: Information Systems, language: English, abstract: This study was conducted to analyze this process closer focusing on a case of Cardiology. Conducting a comprehensive literature review and qualitative expert interviews, the impact of big data in the field of Cardiology was explored. The result of the study shows that big data can play a positive role in three aspects: prediction of disease, prevention of disease and management of disease. Big data enables us to build models that can be used to predict the occurrence of disease. Based on this information, actions can be taken to prevent the disease. Data also helps to manage the disease by offering helpful insights. Medical personnel can retrieve the patient data, with the help of AI, they can make faster decisions allowing them to spend more quality time with the patients and reduce cognitive errors. Through the interviews, it was understood that even though the positive role of big data has been acknowledged, the implementation is still a challenge due to various limitations. The challenges lie mainly on technical know-how and domain knowledge. Further challenges were data security and privacy issues that need to be addressed to mitigate the risks that can be caused by them. The examples of big data implementation in various cases like in heart failure prediction or prevention shows a positive picture. The overwhelming majority of case studies analyzed in this regard show an optimistic picture. Due to growing importance and use of smart devices, IoT, genomics and the recent developments in the field of ICTs, it is expected that big data will not only leave a positive influence on the field of Cardiology, it will also change the way medicine is practiced and healthcare is offered. The statement ‘Data is the new oil’ has been broadly acknowledged due to its wide-ranging importance. Utilizing big data offers a variety of benefits. Although the health sector was late in terms of exploiting the benefits of big data, currently, the adoption is accelerating. Healthcare is increasingly becoming an information science and the implementation of electronic medical records (EMR) and other information systems is growing rapidly. The patient data originating from smart devices and other sources like genomic databases are supporting the healthcare sector offering better healthcare delivery and increasing efficiency, hence saving costs.

Download or read book Predicting Heart Failure written by Kishor Kumar Sadasivuni and published by John Wiley & Sons. This book was released on 2022-04-04 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: PREDICTING HEART FAILURE Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it. This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. Predicting Heart Failure supplies readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. Readers will also find: Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application Summary of the difficulties of wireless sensor communication and power transfer, and the utility of artificial intelligence in cardiology Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure Discussion of the risks and issues associated with the remote monitoring system Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations. Providing the latest research data for the diagnosis and treatment of heart failure, Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.

Download or read book AI and Big Data in Cardiology written by Nicolas Duchateau and published by Springer Nature. This book was released on 2023-05-04 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a detailed technical overview of the use and applications of artificial intelligence (AI), machine learning and big data in cardiology. Recent technological advancements in these fields mean that there is significant gain to be had in applying these methodologies into day-to-day clinical practice. Chapters feature detailed technical reviews and highlight key current challenges and limitations, along with the available techniques to address them for each topic covered. Sample data sets are also included to provide hands-on tutorials for readers using Python-based Jupyter notebooks, and are based upon real-world examples to ensure the reader can develop their confidence in applying these techniques to solve everyday clinical problems. Artificial Intelligence and Big Data in Cardiology systematically describes and technically reviews the latest applications of AI and big data within cardiology. It is ideal for use by the trainee and practicing cardiologist and informatician seeking an up-to-date resource on the topic with which to aid them in developing a thorough understanding of both basic concepts and recent advances in the field.

Download or read book Big Data Analytics for Healthcare written by Pantea Keikhosrokiani and published by Academic Press. This book was released on 2022-05-19 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work. Presents theories, methods and approaches in which data analytic techniques are used for medical data Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases Discusses social, behavioral, and medical fake news analytics for medical information systems

Download or read book Precision Medicine in Cardiovascular Disease Prevention written by Seth S. Martin and published by Springer Nature. This book was released on 2021-07-07 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the current knowledge and potential future developments of precision medicine techniques including artificial intelligence, big data, mobile health, digital health and genetic medicine in the prevention of cardiovascular disease. It reviews the presently used advanced precision medicine techniques and fundamental principles that continue to act as guiding forces for many medical professionals in applying precision and preventative medical techniques in their day-to-day practices. Precision Medicine in Cardiovascular Disease Prevention describes current knowledge and potential future developments in this rapidly expanding field. It therefore provides a valuable resource for all practicing and trainee cardiologists looking to develop their knowledge and integrate precision medicine techniques into their practices.

Download or read book Big Data Big Analytics written by Michael Minelli and published by John Wiley & Sons. This book was released on 2013-01-22 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique prospective on the big data analytics phenomenon for both business and IT professionals The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability. The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics. Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.) Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.

Download or read book Heart Disease Forecasting in Healthcare written by Pathan Ahmed Khan and published by Ary Publisher. This book was released on 2023-03-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heart disease forecasting in healthcare is an important area of research that aims to predict and prevent cardiovascular diseases. The use of machine learning and artificial intelligence techniques to analyze large amounts of electronic health record (EHR) data has shown promising results in identifying risk factors, early detection, and treatment planning. The goal of heart disease forecasting is to improve clinical decision-making, reduce costs, and improve patient outcomes. Medical imaging and biomarkers also play a critical role in predicting heart disease, and researchers are exploring new ways to integrate these data sources with machine learning models. The use of precision medicine in heart disease forecasting can help personalize treatment plans for patients, based on their individual risk factors and genetic profiles. Heart disease forecasting has important implications for population health and chronic disease management, providing healthcare providers with a powerful tool to prevent and manage cardiovascular diseases, which remain a leading cause of death worldwide.

Download or read book Glance of Machine Learning Algorithms for Prediction of Heart Disease written by Dr. P. Sujatha and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the digital world, data has turn out to be a most important asset for all kinds of organization. All industries are relying on the data for their day to day operations. We are living in the era of data driven decisions. Most of the industries are generating huge amount of data, Healthcare is one among the top. Health care industries generates large amount of data. The main challenge in healthcare professional is to analyze the data effectively for better treatment process. Modern approach in the health care is to prevent the disease with early diagnosis instead of treatment after diagnosis. It is possible to provide better healthcare services at lower costs and increase patient satisfaction with the current technologies. Big data analytics, data mining and machine learning are recent technologies used by healthcare professionals to analyze the data quickly and make better decisions on patient's treatment process. These technologies are used to automate the prediction and diagnosis of disease. Heart diseases are the most prominent reason for death in the world. In the last few decades heart diseases have emerged as life threatening disease. It is preventable if the disease is predicted in the early stage. Early prediction and diagnosis of heart disease can save human life. In recent years many research works have been done using data mining and machine learning algorithms to prevent and predict non communicable diseases like cancer, heart disease, stress, diabetes etc. The objective of this study is to provide glimpse about heart disease, types of heart disease, heart disease prediction factors and various Machine learning algorithms for heart disease prediction. This research paper is also analyzing various existing research works in machine learning algorithms to predict heart diseases.

Download or read book Big Data in Psychiatry and Neurology written by Ahmed Moustafa and published by Academic Press. This book was released on 2021-06-11 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer’s disease and Parkinson’s disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders Analyzes methods in using big data to treat psychiatric and neurological disorders Describes the role machine learning can play in the analysis of big data Demonstrates the various methods of gathering big data in medicine Reviews how to apply big data to genetics

Download or read book A Novel Cluster And Rank Based Method For Prediction Of Heart Diseases written by K. Aravinthan and published by Ary Publisher. This book was released on 2023-03-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heart disease is a leading cause of death worldwide, and early prediction is crucial for effective prevention and management. A novel cluster and rank-based method for prediction of heart disease involves using machine learning algorithms to cluster patients based on similar risk factors and rank them based on their likelihood of developing cardiovascular disease. This method utilizes feature selection techniques to identify the most important risk factors and uses a classification model to predict the risk of heart disease based on these factors. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC. This approach has several advantages, including improved accuracy in predicting heart disease risk, the ability to identify subgroups of patients with similar risk profiles, and the potential to integrate data from electronic health records and other sources.

Download or read book Decision Support Framework for Cardiovascular Disease Prediction Using Machine Learning written by Nitten Singh Rajliwall and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinical decision making is an important and frequent task, which physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e., knowledge and experience learnt from experience, their research, related literature, patient cases, etc.) for anticipating or ascertaining health problems based on clinical risk factors, that deem to be the most salient. However, with the inundation of health data, from EHR system, wearable devices, and other systems for monitoring vital parameters, it has become difficult for physicians to make sense of this massive data, particularly, due to confounding and complex characteristics of chronic diseases, and there is a need for more effective clinical prediction approaches to address these challenges. Given the paramount importance of predictive models for managing chronic disease, cardiovascular diseases in particular, this thesis proposes a novel computational predictive modelling framework, based on innovative machine learning and data science approaches that can aid in clinical decision support. The focus of the proposed predictive modelling framework is on interpretable machine learning approaches that consist of interpretable models based on shallow machine learning techniques, such as those based on linear regression and decision trees and their variants, and model-agnostic approaches based on neural networks and deep learning methods but enhanced with appropriate feature engineering and post-hoc explainability. These approaches allow disease prediction models to be deployed in complex clinical settings, including under remote, extreme, and low-resource environments, where data could be small, big, or massive and has several inadequacies in terms of data quality, noise, or missing data. The availability of interpretable models, and model-agnostic approaches enhanced with explainable aspects are important for physicians and medical professionals, as it will increase transparency, trust and confidence in the decision support provided by computer based algorithmic models. This thesis aims to address the research gap that exists in the current ML/AI based disease detection models, particularly, the lack of robust, objective, explainable, interpretable and trustworthy inference available from the computer based decision support tools, with a majority of the performance metrics reported from computer based tools have been limited to quantitative measures such as accuracy, precision, recall, F-measure, AUC, ROC, without any detailed qualitative metrics, that provide insight into how the computer has arrived at a decision, and ability to explain the decision making logic, eliciting trust from the stakeholders using the system. This could be due to the problem that most of the current ML/AI tools were built using mathematically rigorous constructs, designed around black box approaches, which are hard to interpret and explain, and hence the decisions provided by them appear to be coming from a black box, offering little explanation on decision arrived. The research proposed in this thesis is aimed at the development of a breakthrough explainable predictive modelling framework, based on innovative ML/AI algorithms for building CVD disease detection models. The proposed computation framework provides an intelligent and interpretable holistic analytics platform with improved prediction accuracy, and improved interpretability and explainability. The proposed innovation and development can help drive the healthcare system to one that is more patient-centred, and trustworthy, with potential to be tailored for several diseases such as cancer, cardiovascular disease, asthma, traumatic brain injury, dementia, and diabetes. The outcomes of this research based on innovative findings can serve as an example - that the availability of better computer-based decision support tools, with novel computational strategies, which can address a patient's unique clinical/genetic characteristics, can result in better characterization of diseases and at the same time redefine therapeutic strategies. Some of the key contributions from this research include:• Novel disease detection models based on traditional shallow machine learning algorithms, particularly those based on decision trees and their variants. These algorithms have shown to be inherently interpretable and accurate white box models and can serve as the baseline for comparing with previous models proposed in the literature.• Innovative disease detection models based on model agnostic algorithms, such as deep learning networks, but augmented with appropriate pre- processing and post-processing stages to provide better interpretability and explainability and eventually make them an efficient white box model. For an objective comparison of the methods proposed in each of the above stages, several publicly available benchmark clinical datasets, including Cleveland dataset, NHANES dataset and Framingham Heart Study/CHS dataset were used for model building and experimental validation. Although Cardiovascular disease has been selected as the use case and disease under investigation, since it has led to an alarming increase in the burden of disease, almost at the epidemic levels, and is a major health concern in today's world, the findings from this research can lead to meaningful and significant impact towards improved self-management of chronic non-communicable diseases and make a significant contribution towards better public health management.

Download or read book Machine Learning in Cardiovascular Medicine written by Subhi J. Al'Aref and published by Academic Press. This book was released on 2020-11-20 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine. Provides an overview of machine learning, both for a clinical and engineering audience Summarize recent advances in both cardiovascular medicine and artificial intelligence Discusses the advantages of using machine learning for outcomes research and image processing Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

Download or read book Machine Intelligence and Data Analytics for Sustainable Future Smart Cities written by Uttam Ghosh and published by Springer Nature. This book was released on 2021-05-31 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest advances in computational intelligence and data analytics for sustainable future smart cities. It focuses on computational intelligence and data analytics to bring together the smart city and sustainable city endeavors. It also discusses new models, practical solutions and technological advances related to the development and the transformation of cities through machine intelligence and big data models and techniques. This book is helpful for students and researchers as well as practitioners.

Download or read book Pharmacotherapeutic Management of Cardiovascular Disease Complications A Textbook for Medical Students written by A. Bharath Kumar and published by Bentham Science Publishers. This book was released on 2020-09-21 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pharmacotherapeutic Management of Cardiovascular Disease Complications is an essential textbook which comprehensively informs the reader about a broad variety of cardiovascular pathologies and their management through drug therapy. Key Features:- Features 22 chapters, with 17 chapters dedicated to the management of a wide range of cardiomyopathies and related complications- Introduces readers to heart anatomy and physiology, for both medical and pharmacology students- Covers information on cardiovascular disease biomarkers as well as current and new technologies for diagnostic procedures- Provides additional information on different aspects of cardiovascular disease treatment including etiological factors, prevalence, pathogenesis, clinical symptoms, diagnosis and prevention factors, risk screening and complications- Informs readers on the role of the clinical pharmacist in patient lifestyle modification for therapeutic plans, helping to reduce cardiovascular disease burden in clinical practice The broad coverage and easy-to-read organization of the topics covered on the subject make this textbook an ideal reference for medical students and health care professionals such as doctors, nurses, clinical pharmacists, community pharmacists and paramedics.

Download or read book Big Data Computing written by Rajendra Akerkar and published by CRC Press. This book was released on 2013-12-05 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix

Download or read book Big Data Analytics in Healthcare written by Anand J. Kulkarni and published by Springer Nature. This book was released on 2019-10-01 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms.

Download or read book Advanced Machine Learning Technologies and Applications written by Aboul Ella Hassanien and published by Springer Nature. This book was released on 2020-05-25 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the refereed proceedings of the 5th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2020), held at Manipal University Jaipur, India, on February 13 – 15, 2020, and organized in collaboration with the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic and security, as well as intelligence swarms and optimization.