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Reimagining the way work is done through big data, analytics, and event processing, Chris is the cofounder of Successful Workplace. He believes there’s no end to what we can change and improve. Chris is a marketing executive and flew for the US Navy before finding a home in technology 17 years ago. An avid outdoorsman, Chris is also passionate about technology and innovation and speaks frequently about creating great business outcomes at industry events. As well as being a contributor for The TIBCO Blog, Chris contributes to the Harvard Business Review, Venture Beat, Forbes, and the PEX Network. Christopher is a DZone MVB and is not an employee of DZone and has posted 302 posts at DZone. You can read more from them at their website. View Full User Profile

Big data, crowdsourcing and machine learning tackle Parkinson’s

08.05.2013
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Parkinson’s is a very tough disease to fight. People suffering from the disease often have significant tremors that keep them from being able to create accurate records of their daily challenges. Without this information, doctors are unable to fine tune drug dosages and other treatment regimens that can significantly improve the lives of sufferers.

It was a perfect catch-22 situation until recently, when the Michael J. Fox Foundation announced that LIONsolver, a company specializing in machine learning software, was able to differentiate Parkinson’s patients from healthy individuals and to also show the trend in symptoms of the disease over time.

Crowdsourcing Big Data analysis

To set up the competition, the Foundation worked with Kaggle, an organization that specializes in crowdsourced big data analysis competitions. The use of crowdsourcing as a way to get to the heart of very difficult Big Data problems works by allowing people the world over from a myriad of backgrounds and with diverse experiences to devote time on personally chosen challenges where they can bring the most value. It’s a genius idea for bringing some of the scarcest resources together with the most intractable problems.

Machine learning a Big Data solution

To create a solution usingmachine learning, the winning group fromLIONsolverhad to consult with doctors to first figure out what symptoms look like in data form. From that information, they were able to create a training set of data that represented known disease symptoms. Using that training set combined with data streamed from mobile apps worn by patients, LIONsolver’s software was able to learn any individual patient’s particular patterns and provide doctors with highly accurate information that is crucial to appropriate treatment.

Drake Pruitt, CEO of LIONsolver, explains the long-term benefit of this discovery this way:

We see this opportunity as part of an overall trend in healthcare toward applying forecasting and prediction to health record and wellness data, in order to help doctors and their patients achieve healthier lives with manageable healthcare costs. In short: More and more mobile devices are linking with monitoring services to analyze a growing amount of data. This analysis will provide a unique opportunity to take better care of patients, and to teach patients to take better care of themselves.

The takeaways

One of the biggest takeaways from this story is the evidence that Big Data isn’t just hype. Enormous opportunities exist for passive data collection through smartphones and other sensors. We’ve reached a point where the cost of data collection is significantly lower and in this case, was essentially an app running on a common device.

A second takeaway is the power of crowdsourced solutions in areas where resources like data scientists are hard to find and hire through conventional means.

The third significant lesson is the value of machine learning alongside Big Data. Classic data mining skills aren’t effective for every problem and machines learning can be used to find patterns in data that humans can’t. Machines don’t feel the same incentives that scientists feel to prove their own theories and for this reason alone, can be far more effective.

Published at DZone with permission of Christopher Taylor, author and DZone MVB.

(Note: Opinions expressed in this article and its replies are the opinions of their respective authors and not those of DZone, Inc.)