LIKE NEW Anonymizing Health Case Studies and Methods to Get You Started

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Subject Area
Data Analysis
Subject
Computer Science
ISBN
9781449363079
類別

關於產品

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1449363075
ISBN-13
9781449363079
eBay Product ID (ePID)
166714146

Product Key Features

Number of Pages
225 Pages
Publication Name
Anonymizing Health Data : Case Studies and Methods to Get You Started
Language
English
Publication Year
2014
Subject
Social Aspects / General, Security / Online Safety & Privacy, Data Processing
Type
Textbook
Author
Luk Arbuckle, Khaled El Emam
Subject Area
Computers
Format
Trade Paperback

Dimensions

Item Height
0.6 in
Item Weight
16.1 Oz
Item Length
9.1 in
Item Width
7 in

Additional Product Features

Intended Audience
Scholarly & Professional
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
610.72
Synopsis
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, Clinical and research organizations increasingly want to share patient data, and robust de-identification is crucial to meet legal obligations and or ethical reasons. This book, written by two leading experts in de-identification, explains how to adhere to regulations in a defensible way to protect sensitive patient data. Numerous case studies are included from settings that range from typical clinical treatment to disease registries. The metrics used to determine that reidentification is unlikely, and special cases such as continuously released data, will be covered. The authors finish with a discussion of the effects of de-identification on data quality and analysis., This book, written by two leading experts in de-identification, explains how to adhere to regulations in a defensible way to protect sensitive patient data. Numerous case studies are included from settings that range from typical clinical treatment to disease registries.
LC Classification Number
R853.C3

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