Intel To use Big Data To Combat Parkinson's

Aug 14, 2014

Intel is joining forces with charity the Michael J. Fox Foundation to improve Parkinson’s disease monitoring and treatment by using an advanced big data analytics platform.

The new collaboration includes a multiphase research study that aims to use a Big Data analytics platform to recognise notice patterns in information collected from users' wearable technology.

“Emerging technologies can not only create a new paradigm for measurement of Parkinson’s, but, as more data is made available to the medical community, may also point to currently unidentified features of the disease that could lead to new areas of research.” predicted Diane Bryant, senior VP and general manager in the chip giant's Data Center Group.

Advances in data collection and analysis allow the wealth of molecular data available to be correlated with objective clinical characterisation of the disease in order for it to be used in the development of new drugs.

Data collection of this type also has the potential to allow researchers to get a better idea of how the disease progresses over time and its relationship to molecular changes.

Big Data + Cloud + Wearable

Intel and the Foundation, founded in 2001 after the actor revealed he was suffering from the condition, have already untaken a study that involved 16 patients using wearable devices and data scientists are correlating the data with patient diaries to work out how accurate they have been.

The two are planning to release a mobile app later this year that allows patients to report medication intake in addition to how they are feeling, to allow medical researchers to study the effects of medication on motor symptoms.

Intel’s Big Data engine, which integrates various software components, is deployed on a cloud infrastructure and in the near future the platform will be able to store other details such as patient, genome and clinical trial data.

It’s hoped that eventually the platform will allow other advanced techniques, such as machine learning and graph analytics, that bring more accurate predictive models to let researchers detect changes in symptoms and change treatment models.

Author: Jamie Hinks
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