The Michael J. Fox Foundation for Parkinson’s Research (MJFF) and Intel Corporation are collaborating to improve research and treatment for Parkinson’s disease. The partnership includes a multiphase research study using a new big data analytics platform that detects patterns in participant data collected from wearable technologies used to monitor symptoms.
This effort is an important step in enabling researchers and physicians to measure progression of the disease and to speed progress toward breakthroughs in drug development.
"Nearly 200 years after Parkinson's disease was first described by Dr. James Parkinson in 1817, we are still subjectively measuring the disease largely the same way doctors did then," said Todd Sherer, PhD, CEO of The Michael J. Fox Foundation. "Data science and wearable computing hold the potential to transform our ability to capture and objectively measure patients' actual experience of disease, with unprecedented implications for Parkinson's drug development, diagnosis and treatment."
Advances in data collection and analysis now provide the opportunity to expand the value of this wealth of molecular data by correlating it with objective clinical characterization of the disease for use in drug development.
The potential to collect and analyze data from thousands of individuals on measurable features of Parkinson's—such as slowness of movement, tremor and sleep quality—could enable researchers to assemble a better picture of the clinical progression of Parkinson's and track its relationship to molecular changes.
"The variability in Parkinson's symptoms creates unique challenges in monitoring progression of the disease," said Diane Bryant, senior vice president and general manager of Intel's Data Center Group. "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, it may also point to currently unidentified features of the disease that could lead to new areas of research."
Wearables can unobtrusively gather and transmit objective, experiential data in real time—24 hours a day, seven days a week. With this approach, researchers could go from looking at a small number of data points and pencil-and-paper patient diaries collected sporadically, to analyzing hundreds of readings per second from thousands of patients and attaining a critical mass of data to detect patterns and make new discoveries.
MJFF and Intel initiated a study earlier this year to evaluate the usability and accuracy of wearable devices for tracking agreed physiological features from participants and using a big data analytics platform to collect and analyze the data. The participants (16 Parkinson's patients and nine control volunteers) wore the devices during two clinic visits and at home continuously over four days.
Bret Parker, a patient with Parkinson's who participated in the study, commented, "I know that many doctors tell their patients to keep a log to track their Parkinson's. I am not a compliant patient on that front. I pay attention to my Parkinson's, but it’s not everything I am all the time. The wearables did that monitoring for me in a way I didn’t even notice, and the study allowed me to take an active role in the process for developing a cure."
Data scientists are now correlating the data collected to clinical observations and patient diaries to gauge the devices' accuracy, and are developing algorithms to measure symptoms and disease progression.
To analyze the volume of data, more than 300 observations per second from each patient, Intel developed a big data analytics platform that integrates a number of software components including Cloudera CDH—an open-source software platform that collects, stores, and manages data.
The data platform is deployed on a cloud infrastructure optimized on Intel architecture, allowing scientists to focus on research rather than the underlying computing technologies. It supports an analytics application developed by Intel to process and detect changes in the data in real time, so researchers have a way to measure the progression of the disease objectively.
In the near future, the platform could store other types of data such as patient, genome and clinical trial data, as well as enable other advanced techniques such as machine learning and graph analytics to deliver accurate predictive models that researchers could use to detect change in disease symptoms.
These advances could provide unprecedented insights into the nature of Parkinson's disease, helping scientists measure the efficacy of new drugs and assisting physicians with prognostic decisions.