By Luk Arbuckle, Khaled El Eman
This sensible publication will show confirmed tools for anonymizing well-being facts to aid your company percentage significant datasets, with out exposing sufferer id. top specialists Khaled El Emam and Luk Arbuckle stroll you thru a risk-based technique, utilizing case reviews from their efforts to de-identify hundreds and hundreds of datasets.
Clinical information is effective for study and different sorts of analytics, yet making it nameless with no compromising facts caliber is hard. This e-book demonstrates suggestions for dealing with assorted info forms, according to the authors’ reviews with a maternal-child registry, inpatient discharge abstracts, medical insurance claims, digital scientific list databases, and the realm alternate heart catastrophe registry, between others.
comprehend varied equipment for operating with cross-sectional and longitudinal datasets
determine the chance of adversaries who try and re-identify sufferers in anonymized datasets
decrease the scale and complexity of huge datasets with no wasting key details or jeopardizing privacy
Use tips on how to anonymize unstructured free-form textual content data
reduce the hazards inherent in geospatial info, with out omitting serious location-based wellbeing and fitness information
examine how one can anonymize coding info in overall healthiness data
examine the problem of anonymously linking comparable 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