1.

Record Nr.

UNINA9910437476603321

Titolo

Il primato del disegno : sedici disegni di Michelangelo dalla Casa Buonarroti / / [a cura di] Pina Ragionieri

Pubbl/distr/stampa

Firenze, : Polistampa, 2017

Altri autori (Persone)

Michelangelo Buonarroti <1475-1564.>

Soggetti

Drawing - Italy - Florence

Lingua di pubblicazione

Italiano

Formato

Materiale a stampa

Livello bibliografico

Monografia

2.

Record Nr.

UNISA996206436503316

Titolo

African journal of aquatic science

Pubbl/distr/stampa

Grahamstown, South Africa, : National Inquiry Services Centre, ©2000-

ISSN

1727-9364

Soggetti

Limnology

Aquatic ecology

Ecosystem management

Biotic communities

Africa

Aquatic Ecosystems

Periodicals.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Periodico

Note generali

Refereed/Peer-reviewed



3.

Record Nr.

UNINA9910437908803321

Autore

Gkoulalas-Divanis Aris

Titolo

Anonymization of electronic medical records to support clinical analysis / / Aris Gkoulalas-Divanis

Pubbl/distr/stampa

New York, : Springer, 2013

ISBN

9781461456681

1461456681

Edizione

[1st ed. 2013.]

Descrizione fisica

1 online resource (86 p.)

Collana

SpringerBriefs in electrical and computer engineering, , 2191-8112

Altri autori (Persone)

LoukidesGrigorios

Disciplina

651.5042610285

Soggetti

Medical records - Data processing

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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.

Sommario/riassunto

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.