"This book introduces health administrators, nurses, physician assistants, medical students, and data scientists to statistical analysis of electronic health records (EHRs). The future of medicine depends on understanding patterns in EHRs. This book shows how to use EHRs for precision and predictive medicine"--
This book presents the proceedings of the International Conference on Computing Networks, Big Data and IoT [ICCBI 2019], held on December 19–20, 2019 at the Vaigai College of Engineering, Madurai, India. Recent years have witnessed the intertwining development of the Internet of Things and big data, which are increasingly deployed in computer network architecture. As society becomes smarter, it is critical to replace the traditional technologies with modern ICT architectures. In this context, the Internet of Things connects smart objects through the Internet and as a result generates big data. This has led to new computing facilities being developed to derive intelligent decisions in the big data environment. The book covers a variety of topics, including information management, mobile computing and applications, emerging IoT applications, distributed communication networks, cloud computing, and healthcare big data. It also discusses security and privacy issues, network intrusion detection, cryptography, 5G/6G networks, social network analysis, artificial intelligence, human–machine interaction, smart home and smart city applications.
Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing and automatically analysing medical data from imaging systems and IoT sensors. Addressing analytical and legal issues, and research on integration of big data analytics with respect to clinical practice and clinical utility, architectures and clustering techniques for IoT data processing, effective frameworks for removal of misclassified instances, practicality of big data analytics, methodological and technical issues, potential of Hadoop in managing healthcare data is the need of the hour. This book integrates different aspects used in the field of healthcare such as big data, IoT, soft computing, machine learning, augmented reality, organs on chip, personalized drugs, implantable electronics, integration of bio-interfaces, and wearable sensors, devices, practical body area network (BAN) and architectures of web systems. Key Features: Addresses various applications of Medical Big Data and Internet of Medical Things in real time environment Highlights recent innovations, designs, developments and topics of interest in machine learning techniques for classification of medical data Provides background and solutions to existing challenges in Medical Big Data and Internet of Medical Things Provides optimization techniques and programming models to parallelize the computationally intensive tasks in data mining of medical data Discusses interactions, advantages, limitations, challenges and future perspectives of IoT based remote healthcare monitoring systems. Includes data privacy and security analysis of cryptography methods for the Web of Medical Things (WoMT) Presents case studies on the next generation medical chair, electronic nose and pill cam are also presented.
Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data. Covers data analysis, management and security concepts and tools in the healthcare domain Highlights electronic medical health records and patient information records Discusses the different techniques to integrate Big data and Internet-of-Things in healthcare, including machine learning and data mining Includes multidisciplinary contributions in relation to healthcare applications and challenges
"Hidden from consumers, patient medical data has become a multibillion-dollar worldwide trade between our health-care providers, drug companies, and a complex web of middlemen. This great medical-data bazaar sells copies of our prescriptions, hospital records, insurance claims, blood-test results, and more, stripped of names but still containing identifiers such as year of birth, gender, and doctor's name. As computing grows ever more sophisticated, these patient dossiers are increasingly vulnerable to re-identification, which could make them a target for identity thieves or hackers. Paradoxically, comprehensive electronic files for patient treatment--a key reason medical data exists in the first place--remain an elusive goal. Even today, patients and their doctors rarely have easy access to full records that could improve care. In the evolution of medical data, the instinct for profit has outstripped patient needs. This book reveals the previously hidden story of how such a system evolved internationally. This investigative narrative seeks to spark debate on how we can best balance the promise big data offers to advance medicine and improve lives, while preserving the rights and interests of every patients. We, the patients, deserve a say in this discussion. After all, it's our data."--
One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.
Advances are constantly being made in the fields of medicine and healthcare, and keeping abreast of them is not always easy. This book presents the proceedings of the second KES International Conference on Innovation in Medicine and Healthcare (InMed 14), held in San Sebastian, Spain, in July 2014. The conference was attended by researchers and engineers, managers, students and practitioners from a broad spectrum of medically related fields, and this multidisciplinary group discussed the ways in which technological and methodological innovation, knowledge exchange and enterprise can be applied to issues relating to medicine, surgery, healthcare and the issues of an ageing population. A central theme of the conference was smart medical and healthcare systems, which explored how modern intelligent systems can contribute to the solution of problems faced by healthcare and medical practitioners today and addressed the application of the systems. The 43 papers included here provided a useful and interesting reference for anyone requiring an overview of current innovations in healthcare.
Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Health systems electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. This text reflects how the learning health system infrastructure is maturing, and being advanced by health information exchanges (HIEs) with multiple organizations blending their data, or enabling distributed computing. It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery. New opportunities for nursing and call for new skills in research methodologies are being further enabled by new partnerships spanning all sectors.
The Big Data Now anthology is relevant to anyone who creates, collectsor relies upon data. It's not just a technical book or just a businessguide. Data is ubiquitous and it doesn't pay much attention toborders, so we've calibrated our coverage to follow it wherever itgoes. In the first edition of Big Data Now, the O'Reilly team tracked thebirth and early development of data tools and data science. Now, withthis second edition, we're seeing what happens when big data grows up:how it's being applied, where it's playing a role, and theconsequences -- good and bad alike -- of data's ascendance. We've organized the second edition of Big Data Now into five areas: Getting Up to Speed With Big Data -- Essential information on thestructures and definitions of big data. Big Data Tools, Techniques, and Strategies -- Expert guidance forturning big data theories into big data products. The Application of Big Data -- Examples of big data in action,including a look at the downside of data. What to Watch for in Big Data -- Thoughts on how big data will evolveand the role it will play across industries and domains. Big Data and Health Care -- A special section exploring thepossibilities that arise when data and health care come together.
Updated new edition of Ralph Kimball's groundbreaking book ondimensional modeling for data warehousing and businessintelligence! The first edition of Ralph Kimball's The Data WarehouseToolkit introduced the industry to dimensional modeling,and now his books are considered the most authoritative guides inthis space. This new third edition is a complete library of updateddimensional modeling techniques, the most comprehensive collectionever. It covers new and enhanced star schema dimensional modelingpatterns, adds two new chapters on ETL techniques, includes new andexpanded business matrices for 12 case studies, and more. Authored by Ralph Kimball and Margy Ross, known worldwide aseducators, consultants, and influential thought leaders in datawarehousing and business intelligence Begins with fundamental design recommendations and progressesthrough increasingly complex scenarios Presents unique modeling techniques for business applicationssuch as inventory management, procurement, invoicing, accounting,customer relationship management, big data analytics, and more Draws real-world case studies from a variety of industries,including retail sales, financial services, telecommunications,education, health care, insurance, e-commerce, and more Design dimensional databases that are easy to understand andprovide fast query response with The Data WarehouseToolkit: The Definitive Guide to Dimensional Modeling, 3rdEdition.
Substantial empirical evidence of the contribution of social and behavioral factors to functional status and the onset and progression of disease has accumulated over the past few decades. Electronic health records (EHRs) provide crucial information to providers treating individual patients, to health systems, including public health officials, about the health of populations, and to researchers about the determinants of health and the effectiveness of treatment. Inclusion of social and behavioral health domains in EHRs is vital to all three uses. The Health Information Technology for Economic and Clinical Health Act and the Patient Protection and Affordable Care Act place new importance on the widespread adoption and meaningful use of EHRs. "Meaningful use" in a health information technology context refers to the use of EHRs and related technology within a health care organization to achieve specified objectives. Achieving meaningful use also helps determine whether an organization can receive payments from the Medicare EHR Incentive Program or the Medicaid EHR Incentive Program. Capturing Social and Behavioral Domains in Electronic Health Records is the first phase of a two-phase study to identify domains and measures that capture the social determinants of health to inform the development of recommendations for meaningful use of EHRs. This report identifies specific domains to be considered by the Office of the National Coordinator, specifies criteria that should be used in deciding which domains should be included, identifies core social and behavioral domains to be included in all EHRs, and identifies any domains that should be included for specific populations or settings defined by age, socioeconomic status, race/ethnicity, disease, or other characteristics.
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.
This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field.
This textbook begins with an introduction to the US healthcare delivery system, its many systemic challenges and the prior efforts to develop and deploy informatics tools to help overcome those problems. It goes on to discuss health informatics from an historical perspective, its current state and its likely future state now that electronic health record systems are widely deployed, the HL7 Fast Healthcare Interoperability standard is being rapidly accepted as the means to access the data stored in those systems and analytics is increasing being used to gain new knowledge from that aggregated clinical data. It then turns to some of the important and evolving areas of informatics including population and public health, mHealth and big data and analytics. Use cases and case studies are used in all of these discussions to help readers connect the technologies to real world challenges. Effective use of informatics systems and tools by providers and their patients is key to improving the quality, safety and cost of healthcare. With health records now digital, no effective means has existed for sharing them with patients, among the multiple providers who may care for them and for important secondary uses such as public/population health and research. This problem is a topic of congressional discussion and is addressed by the 21st Century Cures Act of 2016 that mandates that electronic health record (EHR) systems offer a patient-facing API. HL7’s Fast Healthcare Interoperability Resources (FHIR) is that API and this is the first comprehensive treatment of the technology and the many ways it is already being used. FHIR is based on web technologies and is thus a far more facile, easy to implement approach that is rapidly gaining acceptance. It is also the basis for a ‘universal health app platform’ that literally has the potential to foster innovation around the data in patient records similar to the app ecosystems smartphones created around the data they store. FHIR app stores have already been opened by Epic and Cerner, the two largest enterprise EHR vendors. Provider facing apps are already being explored to improve EHR usability and support personalized medicine. Medicare and the Veteran’s Administration have announced FHIR app platforms for their patients. Apple’s new IOS 11.3 features the ability for consumers to aggregate their health records on their iPhone using FHIR. Health insurance companies are exploring applications of FHIR to improve service and communication with their providers and patients. SureScripts, the national e-Prescribing network, is using FHIR to help doctors know if their patients are complying with prescriptions. This textbook is for introductory health informatics courses for computer science and health sciences students (e.g. doctors, nurses, PhDs), the current health informatics community, IT professionals interested in learning about the field and practicing healthcare providers. Though this textbook covers an important new technology, it is accessible to non-technical readers including healthcare providers, their patients or anyone interested in the use of healthcare data for improved care, public/population health or research.
This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward.
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Due to the digitization of medical records, more and more health data is readily available. This dynamic has created many opportunities to unlock this information and use it to improve medical practice, and through research and surveillance understand the effectiveness and side effects of drugs and medical devices to ultimately improve the public's health. This data can also be used for commercial purposes such as sales and marketing. However, this newfound utility raises some profound questions about how this data ought to be used and how it will impact personal privacy. Unless we are able to address these privacy issues in a convincing and defensible way, there will be increased breaches of personal privacy. This will provoke regulators to impose new rules limiting the use and disclosure of health data for secondary purposes, patients increasingly to adopt privacy protective behaviours because they no longer trust how their health information is being managed, or healthcare providers to be reluctant to share their patients' data. By adopting responsible data sharing practices, researchers, companies and the general public can gain the benefits and the promise of big data analytics without sacrificing personal privacy or infringing upon law or regulation. Risky Business - Sharing Health Data While Protecting Privacy illustrates how this goal can be achieved. Bringing articles from a diverse collection of health data experts to inform the reader on contemporary policy, legal and technical issues surrounding health information privacy and data sharing. It is a uniquely practical work to inform the reader on how best - and how not to - share health data in the US and Canada.
Academic Paper from the year 2018 in the subject Computer Sciences - Industry 4.0, grade: A: 90/100 ODER 1,0, Lund University (Informatik), course: Business Intelligence, language: English, abstract: This seminar paper discusses how big data analytics might support healthcare organizations (e.g., hospitals) in medical diagnosis. The paper proceeds as follows: First, an overview of big data analytics in healthcare is provided with a focus on medical image analytics. Second, two large-scale image analysis cases are presented to materialize the theory upon which an integrated framework is proposed that illustrates how big data analytics might assist medical diagnosis. Third, the contemporary challenges of IT adoption in healthcare are discussed, and lastly, a brief conclusion is drawn.