By Luk Arbuckle, Khaled El Eman
This sensible ebook will show confirmed tools for anonymizing health and wellbeing info to assist your company percentage significant datasets, with out exposing sufferer id. best specialists Khaled El Emam and Luk Arbuckle stroll you thru a risk-based technique, utilizing case experiences from their efforts to de-identify countless numbers of datasets.
Clinical information is effective for examine and different forms of analytics, yet making it nameless with out compromising facts caliber is difficult. This e-book demonstrates recommendations for dealing with diversified facts forms, according to the authors’ reviews with a maternal-child registry, inpatient discharge abstracts, medical insurance claims, digital scientific list databases, and the area exchange heart catastrophe registry, between others.
comprehend assorted equipment for operating with cross-sectional and longitudinal datasets
verify the danger of adversaries who try to re-identify sufferers in anonymized datasets
decrease the dimensions and complexity of huge datasets with out wasting key details or jeopardizing privacy
Use how you can anonymize unstructured free-form textual content data
reduce the dangers inherent in geospatial facts, with out omitting severe location-based well-being information
examine how you can anonymize coding info in wellbeing and fitness data
research the problem of anonymously linking similar datasets
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Additional info for Anonymizing Health Data: Case Studies and Methods to Get You Started
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Anonymizing Health Data: Case Studies and Methods to Get You Started by Luk Arbuckle, Khaled El Eman