The World Health Organization considers major depressive disorder (MDD) the third-highest cause of disease burden worldwide, and the highest in the developed world.
Dr. Pathak has developed analytical methods for identifying MDD using the comprehensive and longitudinal NYC-CDRN electronic health record (EHR) systems data, claims data, online social media data from Twitter and PatientsLikeMe, and geo-coded neighborhood and environmental data. These robust datasets facilitated the design of a computation platform that integrated web search, social media, neighborhood conditions, and healthcare data for MDD. He developed computational models that identified community-level risk/protective factors for MDD and studies the impact of these factors on access and utilization of healthcare services.
The effective methods for detection of depressive behavior provided insight not only at an individual-level, but also at a community-level. These methods have helped promote early recognition and treatment of depressive behavior and systems, improving the overall function and productivity of patients.