Mobile Technologies and Machine Learning to Improve Drug Development for Multiple Sclerosis
January 26, 2017
Professor Atif Memon has received funding from the National Institute of Neurological Disorders and Stroke for enhancing sensitivity of combinatorial clinical outcomes for progressive multiple sclerosis by incorporating naturalistic granular data collected by smartphone/tablet/wearables technologies. Current drug development for progressive multiple sclerosis (MS) is limited due to inability to measure with high sensitivity and specificity loss of neurological functions caused by damage to brain and spinal cord. Memon will work towards enhancing the sensitivity of such combinatorial clinical scale(s) by shifting from episodic measurements in the clinic to much denser measurements at patient’s homes using smartphone/tablet/wearables technologies.
Memon and scientists from the NINDS will prototype software modules to measure specific neurological functions using smartphone/tablets in conjunction with wearable technologies. They will deploy, evaluate, and improve the modules based on patient’s feedback, level of measured variation in healthy versus diseased population and correlation with established clinic-based disability measures. Using machine learning, they will aggregate validated quantitative data from different modules into combinatorial scale(s) that will be integrated with- and evaluated against the best clinic-based scales.
The Department welcomes comments, suggestions and corrections. Send email to editor [at] cs.umd.edu.