Anonymizing Health Data: Case Studies and Methods to Get You Started
Emam, Khaled El; Arbuckle, Luk
Language: English
Published by O'Reilly Media, 2014
- Softcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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In English.
Seller Inventory # ria9781449363079_new
- Title
- Anonymizing Health Data: Case Studies and Methods to Get You Started
- Author
- Emam, Khaled El; Arbuckle, Luk
- Publisher
- O'Reilly Media
- Publication year
- 2014
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1449363075
- ISBN 13
- 9781449363079
- Item weight
- 518 grams
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
"Synopsis" may belong to another edition of this title.
About the Author
Luk Arbuckle has been crunching numbers for a decade. He originally plied his trade in the area of image processing and analysis, and then in the area of applied statistics. Since joining the Electronic Health Information Laboratory (EHIL) at the CHEO Research Institute he has worked on methods to de-identify health data, participated in the development and evaluation of secure computation protocols, and provided all manner of statistical support. As a consultant with Privacy Analytics, he has also been heavily involved in conducting risk analyses on the re-identification of patients in health data.
"About the title" may belong to another edition of this title.
Ria Christie Collections
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