health data processing

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Health Data Processing
Author : Marius Fieschi
Publisher : Elsevier
Release Date : 2018-07-13
ISBN 10 : 0081027583
Pages : 340 pages
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Health Data Processing: Systemic Approaches focuses on the design of health information systems and touches on the main themes of medical informatics and public health. The book is written for health professionals in practice or training, and is especially useful for decision-makers or future decision-makers in the field of health information systems. Users will find sections on the question of reusing data for other purposes, protection of individual liberties that this data and technologies make more acute, and the irruption of large masses of genetic data and its related problems. This book develops the methodological and conceptual aspects related to these issues. Proposes a methodology for the development of health information systems for the better use of digital technologies Illustrates a systemic, transversal, conceptual vision that supports the complex reality of the healthcare world, where the interoperability of agents (professionals and software) is central Discusses the reuse of resources of data for knowledge improvement, health security and public health

Health Data Processing
Author : Marius Fieschi
Publisher : Elsevier
Release Date : 2018-07-13
ISBN 10 : 0081027583
Pages : 340 pages
GET BOOK!

Health Data Processing: Systemic Approaches focuses on the design of health information systems and touches on the main themes of medical informatics and public health. The book is written for health professionals in practice or training, and is especially useful for decision-makers or future decision-makers in the field of health information systems. Users will find sections on the question of reusing data for other purposes, protection of individual liberties that this data and technologies make more acute, and the irruption of large masses of genetic data and its related problems. This book develops the methodological and conceptual aspects related to these issues. Proposes a methodology for the development of health information systems for the better use of digital technologies Illustrates a systemic, transversal, conceptual vision that supports the complex reality of the healthcare world, where the interoperability of agents (professionals and software) is central Discusses the reuse of resources of data for knowledge improvement, health security and public health

Directory of Automatic Data Processing Systems in the Public Health Service
Author : United States. Public Health Service
Publisher : N.A
Release Date : 1978
ISBN 10 :
Pages : 329 pages
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Health Data Privacy under the GDPR
Author : Maria Tzanou
Publisher : Routledge
Release Date : 2020-11-24
ISBN 10 : 0429663846
Pages : 166 pages
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The growth of data-collecting goods and services, such as ehealth and mhealth apps, smart watches, mobile fitness and dieting apps, electronic skin and ingestible tech, combined with recent technological developments such as increased capacity of data storage, artificial intelligence and smart algorithms, has spawned a big data revolution that has reshaped how we understand and approach health data. Recently the COVID-19 pandemic has foregrounded a variety of data privacy issues. The collection, storage, sharing and analysis of health- related data raises major legal and ethical questions relating to privacy, data protection, profiling, discrimination, surveillance, personal autonomy and dignity. This book examines health privacy questions in light of the General Data Protection Regulation (GDPR) and the general data privacy legal framework of the European Union (EU). The GDPR is a complex and evolving body of law that aims to deal with several technological and societal health data privacy problems, while safeguarding public health interests and addressing its internal gaps and uncertainties. The book answers a diverse range of questions including: What role can the GDPR play in regulating health surveillance and big (health) data analytics? Can it catch up with internet-age developments? Are the solutions to the challenges posed by big health data to be found in the law? Does the GDPR provide adequate tools and mechanisms to ensure public health objectives and the effective protection of privacy? How does the GDPR deal with data that concern children’s health and academic research? By analysing a number of diverse questions concerning big health data under the GDPR from various perspectives, this book will appeal to those interested in privacy, data protection, big data, health sciences, information technology, the GDPR, EU and human rights law.

Data and Text Processing for Health and Life Sciences
Author : Francisco M. Couto
Publisher : Springer
Release Date : 2019-06-10
ISBN 10 : 3030138453
Pages : 98 pages
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This open access book is a step-by-step introduction on how shell scripting can help solve many of the data processing tasks that Health and Life specialists face everyday with minimal software dependencies. The examples presented in the book show how simple command line tools can be used and combined to retrieve data and text from web resources, to filter and mine literature, and to explore the semantics encoded in biomedical ontologies. To store data this book relies on open standard text file formats, such as TSV, CSV, XML, and OWL, that can be open by any text editor or spreadsheet application. The first two chapters, Introduction and Resources, provide a brief introduction to the shell scripting and describe popular data resources in Health and Life Sciences. The third chapter, Data Retrieval, starts by introducing a common data processing task that involves multiple data resources. Then, this chapter explains how to automate each step of that task by introducing the required commands line tools one by one. The fourth chapter, Text Processing, shows how to filter and analyze text by using simple string matching techniques and regular expressions. The last chapter, Semantic Processing, shows how XPath queries and shell scripting is able to process complex data, such as the graphs used to specify ontologies. Besides being almost immutable for more than four decades and being available in most of our personal computers, shell scripting is relatively easy to learn by Health and Life specialists as a sequence of independent commands. Comprehending them is like conducting a new laboratory protocol by testing and understanding its procedural steps and variables, and combining their intermediate results. Thus, this book is particularly relevant to Health and Life specialists or students that want to easily learn how to process data and text, and which in return may facilitate and inspire them to acquire deeper bioinformatics skills in the future.

Big Data in Medical Image Processing
Author : R. Suganya,S. Rajaram,A. Sheik Abdullah
Publisher : CRC Press
Release Date : 2018-01-29
ISBN 10 : 1351366610
Pages : 202 pages
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The field of medical imaging seen rapid development over the last two decades and has consequently revolutionized the way in which modern medicine is practiced. Diseases and their symptoms are constantly changing therefore continuous updating is necessary for the data to be relevant. Diseases fall into different categories, even a small difference in symptoms may result in categorising it in a different group altogether. Thus analysing data accurately is of critical importance. This book concentrates on diagnosing diseases like cancer or tumor from different modalities of images. This book is divided into the following domains: Importance of big data in medical imaging, pre-processing, image registration, feature extraction, classification and retrieval. It is further supplemented by the medical analyst for a continuous treatment process. The book provides an automated system that could retrieve images based on user’s interest to a point of providing decision support. It will help medical analysts to take informed decisions before planning treatment and surgery. It will also be useful to researchers who are working in problems involved in medical imaging.

Big Data on Real-World Applications
Author : Sebastian Ventura Soto,José Luna,Alberto Cano
Publisher : BoD – Books on Demand
Release Date : 2016-07-20
ISBN 10 : 9535124897
Pages : 124 pages
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As technology advances, high volumes of valuable data are generated day by day in modern organizations. The management of such huge volumes of data has become a priority in these organizations, requiring new techniques for data management and data analysis in Big Data environments. These environments encompass many different fields including medicine, education data, and recommender systems. The aim of this book is to provide the reader with a variety of fields and systems where the analysis and management of Big Data are essential. This book describes the importance of the Big Data era and how existing information systems are required to be adapted to face up the problems derived from the management of massive datasets.

Health Data in the Information Age
Author : Institute of Medicine,Committee on Regional Health Data Networks
Publisher : National Academies Press
Release Date : 1994-01-01
ISBN 10 : 0309049954
Pages : 272 pages
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Regional health care databases are being established around the country with the goal of providing timely and useful information to policymakers, physicians, and patients. But their emergence is raising important and sometimes controversial questions about the collection, quality, and appropriate use of health care data. Based on experience with databases now in operation and in development, Health Data in the Information Age provides a clear set of guidelines and principles for exploiting the potential benefits of aggregated health data--without jeopardizing confidentiality. A panel of experts identifies characteristics of emerging health database organizations (HDOs). The committee explores how HDOs can maintain the quality of their data, what policies and practices they should adopt, how they can prepare for linkages with computer-based patient records, and how diverse groups from researchers to health care administrators might use aggregated data. Health Data in the Information Age offers frank analysis and guidelines that will be invaluable to anyone interested in the operation of health care databases.

High-Performance Modelling and Simulation for Big Data Applications
Author : Joanna Kołodziej,Horacio González-Vélez
Publisher : Springer
Release Date : 2019-03-25
ISBN 10 : 3030162729
Pages : 352 pages
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This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.

Anonymizing Health Data
Author : Khaled El Emam,Luk Arbuckle
Publisher : "O'Reilly Media, Inc."
Release Date : 2013-12-11
ISBN 10 : 1449363032
Pages : 228 pages
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Updated as of August 2014, this practical book will demonstrate proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets. Clinical data is valuable for research and other types of analytics, but making it anonymous without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors’ experiences with a maternal-child registry, inpatient discharge abstracts, health insurance claims, electronic medical record databases, and the World Trade Center disaster registry, among others. Understand different methods for working with cross-sectional and longitudinal datasets Assess the risk of adversaries who attempt to re-identify patients in anonymized datasets Reduce the size and complexity of massive datasets without losing key information or jeopardizing privacy Use methods to anonymize unstructured free-form text data Minimize the risks inherent in geospatial data, without omitting critical location-based health information Look at ways to anonymize coding information in health data Learn the challenge of anonymously linking related datasets

Index of NLM Serial Titles
Author : National Library of Medicine (U.S.)
Publisher : N.A
Release Date : 1984
ISBN 10 :
Pages : 329 pages
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A keyword listing of serial titles currently received by the National Library of Medicine.

Healthcare Data Analytics and Management
Author : Nilanjan Dey,Amira S. Ashour,Simon James Fong, PhD,Chintan Bhatt, Ph.D
Publisher : Academic Press
Release Date : 2018-11-30
ISBN 10 : 9780128153680
Pages : 340 pages
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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