Current Projects



  • Qiao, S., Li, Z., Liang, C., Li, X., & Rudisill, C. (under review). Risk perception of COVID-19 and its socioeconomic correlates in the United States: A social media analysis. full text

Deep Learning Assisted Opioid Use Disorder Diagnosis Using EHR data

Electronic Health Records (EHR) hold great promise in assisting providers to identify individuals with possible opioid use disorder (OUD), treatment referral, as well as for onsite buprenorphine initiation protocols that are highly effective in the field. We develop Deep Learning models to assist providers in OUD diagnosis by harnessing individuals’ chronological EHR using clinical Natural Language Processing (NLP).

Funding: Prisma Health

The heterogeneous contributing factors found in preventable medication-related harms suggest that intervention strategies (e.g., patient coaching/education, discharge instructions, coordination, and medication reconciliation) should reflect personalized needs of patients and context-specific discharge. We use Electronic Health Records (EHR) chart review to collect clinical decision rules about personalized information to be provided by clinicians. We then develop Deep Learning models to automatically learn from EHR and clinical decision rules to suggest personalized information at discharge.

Funding: Prisma Health