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Archive for the ‘Computation’ Category

Fight malaria by contributing your computer while you sleep

Posted by evolvingwheel on February 16, 2008

Remember the SETI@Home project that was launched in 1999? It has been one of those pioneering projects where grid computing was brought to average consumers who would donate their computer hours for complex analysis of radio-signals from the space. The down-loadable software would receive data from the SETI server in the background and do calculations when the machine is idle at night or when you are away from it. Such grid computing way back then opened the doors for a new class of utility that would one day serve for complex stochastic simulations that could take years with limited computing power. A new possibility emerged!

Paying heed to this tremendous potential of volunteered grid computing across populations with desktops/laptos, MalariaControl.net hosted by AFRICA@HOME has launched a similar grid computing effort to model transmission dynamics and health effects on demographics with potential of getting infected by malaria. These simulations are intensive as they demand a huge set of grid points (human populations) with innumerable attributes that control the derivation of disease progression trajectory and other forecasting knowledge. The idea is simple. The Swiss Tropical Institute has developed a model for malaria epidemiology that uses the volunteer computing ability to calculate a credible analysis and a platform for evidence-based-treatment for malaria in Africa. malaria_kid.jpg

Again, the possibility of grid computing with volunteer computer hours is enormous. Further, to sweeten the deal, the architecture to support such efforts is FREE! The Berkeley Open Architecture for Network Computing (BOINC) is a openly available middleware that can be used to launch any such project. Another greatness of the BOINC framework is the support for both Microsoft Windows and Unix/Linux systems. Even though disease epidemiology studies for non-profit endeavors are great objectives for harnessing BOINC-like utility, the for-profit sector can also benefit from such volunteer contributions. Market research based on demographic behaviors and activity patterns across international borders is a demanding aspect of international business today. Stochastic marketing research models can leverage such grid computing efforts to run comprehensive analysis of product usage patterns. Furthermore, the volunteer attribute can be enriched by a monetary payment factor for hours of modeling usage of one’s computer too.

Picture: Courtesy AMREF

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Posted by evolvingwheel on March 16, 2007

I couldn’t help being excited about the option – an invaluable opportunity to save lives and prevent collateral damages. You should check out this [article] in TechReview. John Guttag, head of the Dept. of Electrical Eng. and Computer Sc. at MIT, along with a student is in the process of developing an early warning system for seizures and other health risks by analyzing real time medical data from the body. The details of the development can be found in the text. I will talk a little bit about possibilities and risks associated with the commercialization of such detectors.

The system analyzes real time medical data from the body and feeds it to a highly sophisticated algorithm that processes the pattern. The tool is fed with several patterns that result to death or near severe outcomes. The system then matches the current diagnostics with possible red-alert patterns and tries to beep you about a highly probable circumstance. The idea in practice is noble. However, there are several factors that need to be considered.

In industrial early warning systems, data mostly respond to mechanical outcomes that are predominantly governed by linear factor based rules. Even in polynomial representations, the changes in conditions correspond to a graphical pattern that consistently extends to a proven outcome. However, in human body, the response is dependent on individual human factors and unique physical conditions. If the tool records thousands of graphs and tries to match it with a sudden peak in sugar level or a clot in Mr. X, it may not necessarily lead to the same outcome. Again, on the contrary, the large sampling data and high probability of an event might correlate and the outcome could still be matched. The thin line separating a success from a failure should be considered diligently.

Next, the opportunity for computation is enormous. If there is a service running supercomputers and analyzing data and sends the result securely to the attached device right on time, it could save so many so much. But, would the services be privately available? Who will control the quality control for highly sensitive medical diagnosis? How will a consumer choose a service? Who will validate the consistency?

Well, I hope we have answers to all these issues soon and see such a product in the market!

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