Data, Technology Drive New METHODS TO Parkinson’s Care

Parkinson’s disease is an intensifying neurological disorder that erodes a person’s control over their motions and speech. Even though many of the recent advancements in treatment have transformed Parkinson’s into a controllable chronic illness, the individual patient experience can vary widely in both the onset and progression of the symptoms of the condition. This creates problems for clinicians who must constantly tweak the combination and doses of medications to effectively manage symptoms and researchers who are often confronted with a range of responses to experimental treatments. A good example of this process is new research out in the journal The Lancet Neurology.

Technology (CHeT), and GNS Healthcare, and was funded by the Michael J. Fox Foundation for Parkinson’s Research and the National Institute of Neurological Disorders and Stroke. The analysts tapped into huge data sets published by the Parkinson’s Progression Markers Initiative (PPMI) which includes collected biological samples and clinical data from a huge selection of individuals with the condition. Within a departure from traditional research techniques, the team transformed within the huge levels of hereditary, clinical, and imaging profiles published by the PPMI study to machine learning and simulation program.

As the computer program analyzed the info, it was also “learning” by constantly refining and changing its criteria and algorithms as it sifted through the info looking for patterns and associations. The study determined a mutation in the LINGO2 gene that, collectively with another gene and demographic factors, could identify patients with faster electric motor progression of Parkinson’s. The finding, if verified, could eventually help clinicians to refine treatment and help research workers more precisely know how individual patients may react to experimental therapies. The application of data-driven systems to biomedical research has exploded in the last many years. URMC neurologist Ray Dorsey, M.D., M.B.A., who is the director of CHeT also, has been at the forefront of the transformation.

Dorsey has long been a pioneer in expanding the usage of Parkinson’s treatment via telemedicine. In 2015, Dorsey – in collaboration with Sage Bionetworks – helped develop an iPhone app that allows patients with Parkinson’s disease to monitor their symptoms instantly and share these details with researchers. Several additional research programs at the intersection of disease and technology have surfaced in Rochester lately.

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Gaurav Sharma, Ph.D., a teacher in the University of Rochester Department of Electrical and Computer Engineering, is working with wearable sensors to monitor the development of Parkinson’s and Huntington’s diseases. M. Eshan Hoque, Ph.D., an assistant teacher in the Department of Computer Science, is developing analytical tools that scan videos of patients to help detect early-stage Parkinson’s.

The problem for research workers is to both transform the vast amount of data that is being collected into a usable format – an activity referred to as data wrangling – and then eventually extract valuable medical and scientific conclusions. To do this, new methods and tools to get, store, organize, and analyze data are being developed. Lately, the INFIRMARY and the University have made significant new investments in state-of-the-art computational resources, recruited new faculty, and started new degree programs in the fields of bioinformatics, computer science, and data research. The data trend in medicine has created an influx of new scholarship or grant. Dorsey also acts as editor-in-chief of Digital Biomarkers, a fresh journal that launched this month in acknowledgement that emerging technologies hold the potential to change research and the delivery of treatment.