rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Anonymization of Electronic Medical Records to Support Clinical Analysis
  name:
    name part: Gkoulalas-Divanis, Aris.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Loukides, Grigorios.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 72 p. 23 illus. online resource.
  abstract: Anonymization of Electronic Medical Records to Support Clinical Analysis closely examines the privacy threats that may arise from medical data sharing, and surveys the state-of-the-art methods developed to safeguard data against these threats. To motivate the need for computational methods, the book first explores the main challenges facing the privacy-protection of medical data using the existing policies, practices and regulations. Then, it takes an in-depth look at the popular computational privacy-preserving methods that have been developed for demographic, clinical and genomic data sharing, and closely analyzes the privacy principles behind these methods, as well as the optimization and algorithmic strategies that they employ. Finally, through a series of in-depth case studies that highlight data from the US Census as well as the Vanderbilt University Medical Center, the book outlines a new, innovative class of privacy-preserving methods designed to ensure the integrity of transferred medical data for subsequent analysis, such as discovering or validating associations between clinical and genomic information. Anonymization of Electronic Medical Records to Support Clinical Analysis is intended for professionals as a reference guide for safeguarding the privacy and data integrity of sensitive medical records. Academics and other research scientists will also find the book invaluable.
  table of contents: Introduction -- Overview of patient data anonymization -- Re-identification of clinical data through diagnosis information -- Preventing re-identification while supporting GWAS -- Case study on electronic medical records data -- Conclusions and open research challenges -- Index.
  note: by Aris Gkoulalas-Divanis, Grigorios Loukides.
  subject:
    topic: Computer science
  subject:
    topic: Health informatics
  subject:
    topic: Data mining
  subject:
    topic: Information storage and retrieval
  subject:
    topic: Computer Science
  subject:
    topic: Health Informatics
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Information Storage and Retrieval
  classification: R858-R859.7
  classification: 502.85
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Electrical and Computer Engineering
  identifier: 9781461456681
  identifier: http://dx.doi.org/10.1007/978-1-4614-5668-1
  record info:
    record creation date: 121026
    record change date: 20151204144041.0
    record identifier: 978-1-4614-5668-1