Contact Project Developer Ashish D. Tiwari [astiwz@gmail.com]
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Big Data- Big Knowledge Big Data for Personalized Healthcare

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 researc
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Big Data- Big Knowledge Big Data for Personalized Healthcare

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 non textual 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.

Existing System:

THE birth of big data, as a concept if not as a term, is usually associated with a META Group report by Doug Laney entitled “3-D Data Management: Controlling Data Volume, Velocity, and Variety” published in 2001 [1]. Further developments now suggest big data problems are identified by the so-called volume (quantity of data), variety (data from different categories), velocity (fast generation of new data), veracity (quality of the data), and value (in the data).

Proposed System:

This may be conceptually simple, the VPH vision contains a tremendous challenge, namely, the development of mathematical models capable of accurately predicting what will happen to a biological system. To tackle this huge challenge, multifaceted research is necessary: around medical imaging and sensing technologies (to produce quantitative data about the patient’s anatomy and physiology) ,data processing to extract from such data information that in some cases is not immediately available ,biomedical modeling to capture the available knowledge into predictive.

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