Big Data, Big Knowledge: Big Data for Personalized Healthcare

Marco Viceconti, Peter Hunter, Rod Hose

Research output: Contribution to journalArticle

Abstract

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.

Original languageEnglish
Article number7047725
Pages (from-to)1209-1215
Number of pages7
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number4
DOIs
Publication statusPublished - Jul 1 2015

Keywords

  • big data
  • healthcare
  • Virtual Physiological Human

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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