Informatics Research Seminar: Standard Terminologies of Adverse Effects and Their use in Radiation Therapy clinical trials

September 9 @ 4:00 – 5:00 pm

 

Speaker: Yaorong Ge, PhD
Presented from UNC-C

Broadcast Link: Seminar

 

Abstract:

A critical step toward big data analytics is to standardize data elements and their values across multiple studies and institutions. In radiation therapy, adverse events data are critically important for technology assessment, quality management, and effectiveness research. Three of the most commonly used standards for reporting adverse events in RT clinical studies are the Common Terminology Criteria for Adverse Events (CTCAE), the Radiation Therapy Oncology Group (RTOG), and the Late Effect Normal Tissue Task Force (LENT)/subjective, objective, management, analytic (SOMA). The CTCAE is strongly promoted as the standard for adverse event reporting in recent years. However, there is a lack of studies to understand how the various standards have been used in RT clinical studies and therefore lack of understanding as to how these standards should be adopted and improved. Multiple standards that are not properly harmonized can lead to significant difficulties in large-scale data integration and analysis. In this talk we will briefly introduce the various adverse events standards and then describe a text mining based method for investigating the usage trend of three most commonly used standards in published literature related to radiation therapy clinical studies. Finally, we will report some of our early findings and validation results.

 

Biosketch:

Yaorong Ge, PhD is an associate professor in the Health Informatics Program in the College of Computing and Informatics of UNC Charlotte. Prior to UNCC, he was a faculty member in the Department of Biomedical Engineering at Wake Forest University Health Sciences where he developed technologies for virtual colonoscopy, structured reporting, image management and sharing, and clinical data analytics. Dr. Ge’s current research focuses on integration and analysis of large-scale healthcare data for decision support and rapid learning. In recent years, he has been working on integration and modeling of radiation therapy planning data and guideline knowledge in collaboration with colleagues in the Department of Radiation Oncology at Duke University Medical Center. Dr. Ge received a PhD in Computer Science from Vanderbilt University.