Development of Computational Modeling to Identify Symptom Changes in Schizophrenia and Depression
Philip Resnik (UMD, College of Arts and Humanities, UMIACS) & Deanna Kelly (UMB, School of Medicine)
Identifying and monitoring mental illness is an enormous societal challenge: in addition to high costs,continued access to care and regular treatment and monitoring is fragmented, and more than 89 million Americans do not even have ready access to a clinician qualified to perform psychiatric or psychological evaluations. This project brings together Dr. Kelly’s expertise in the treatment and monitoring of severe mental illness, particularly schizophrenia, with Dr. Resnik’s expertise in the use of linguistic analysis and computational modeling of mental status, including work in depression and PTSD. The project will collect a unique new dataset including clinical variables, within-clinic prompted language responses, and naturally occurring social media interaction. Using this dataset, new computational techniques for predictive modeling will be developed, with a focus on symptom changes within clinically relevant symptom domains.
Geospatial Mapping and Access to Cancer Screening Services in Nigeria, a Low and Middle Income Country (LMIC)
Kathleen Stewart (UMD, College of Behavioral and Social Sciences) & Clement Adebamowo (UMB, School of Medicine)
This study combines expertise in cancer epidemiology (Adebamowo) and geographic information science (Stewart) to apply geospatial information technologies to determine spatial accessibility and utilization of cervical cancer prevention services for Keffi LGA, a region in north-central Nigeria. This research will investigate population characteristics, travel networks and possible spatial barriers, and compute spatial accessibility indices for the region in order to understand how to improve the uptake of cervical cancer prevention services and determine optimal locations of prevention services for this part of Nigeria.