Literature DB >> 12605543

Privacy issues in personalized medicine.

Laszlo T Vaszar1, Mildred K Cho, Thomas A Raffin.   

Abstract

Pharmacogenomics is the emerging study of why individuals respond differently to drugs. It aims to replace the current 'one size fits all' therapeutic approach with 'personalized medicine' that will use pharmacogenomic tests to predict drug response. In a simple conceptualization, these tests challenge privacy as a result of two factors: how comprehensive is the test and how is the access to samples or digital information controlled. Point-of-care tests are likely to be limited in scope, fit seamlessly into medical records and do not raise qualitatively new ethical and privacy challenges. In order to define practically relevant pharmacogenomic predictive patterns however, large-scale clinical trials and research on human specimens will be required, resulting in large databases of genomic information. The genomic scans' magnitude, stability, implications to kin and ease of dissemination together represent a qualitatively different challenge compared to traditional, self-limited and often temporally transient medical information.

Entities:  

Keywords:  Genetics and Reproduction; Health Care and Public Health

Mesh:

Year:  2003        PMID: 12605543     DOI: 10.1517/phgs.4.2.107.22625

Source DB:  PubMed          Journal:  Pharmacogenomics        ISSN: 1462-2416            Impact factor:   2.533


  4 in total

1.  An evaluation of the current state of genomic data privacy protection technology and a roadmap for the future.

Authors:  Bradley A Malin
Journal:  J Am Med Inform Assoc       Date:  2004-10-18       Impact factor: 4.497

2.  Grid Binary LOgistic REgression (GLORE): building shared models without sharing data.

Authors:  Yuan Wu; Xiaoqian Jiang; Jihoon Kim; Lucila Ohno-Machado
Journal:  J Am Med Inform Assoc       Date:  2012-04-17       Impact factor: 4.497

3.  EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning.

Authors:  Tsung-Ting Kuo; Rodney A Gabriel; Krishna R Cidambi; Lucila Ohno-Machado
Journal:  J Am Med Inform Assoc       Date:  2020-05-01       Impact factor: 4.497

4.  Fair compute loads enabled by blockchain: sharing models by alternating client and server roles.

Authors:  Tsung-Ting Kuo; Rodney A Gabriel; Lucila Ohno-Machado
Journal:  J Am Med Inform Assoc       Date:  2019-05-01       Impact factor: 4.497

  4 in total

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