Leveraging Centralized Health System Data Management and Large Language Model–Based Data Preprocessing to Identify Predictors for Radiation Therapy Interruption
Automated data preprocessing permitted efficient identification of treatment course length, marital status, disease site, Medicaid coverage, and socially vulnerable locations as significant factors associated with RTI. These findings underscore the need for data-driven risk assessment and intervention strategies to maintain cancer treatment quality at scale.
Authors
- Rezaur Rashid (ORCID: https://orcid.org/0000-0003-1343-5364)
- Janet A. Zink (ORCID: https://orcid.org/0000-0002-0261-4917)
- Arash Shaban‐Nejad (ORCID: https://orcid.org/0000-0003-2047-4759)
- Lokesh K. Chinthala (ORCID: https://orcid.org/0000-0002-1258-8353)
- Fekede Asefa Kumsa (ORCID: https://orcid.org/0000-0002-6700-4810)
- Soheil Hashtarkhani (ORCID: https://orcid.org/0000-0001-7750-6294)
- Christopher Brett (ORCID: https://orcid.org/0000-0003-2958-599X)
- Robert Lowell Davis (ORCID: https://orcid.org/0000-0001-8807-0019)
- David L. Schwartz (ORCID: https://orcid.org/0000-0002-7235-5586)
Institutions
- University of Tennessee Health Science Center (US)
- University of Tennessee at Knoxville (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1200/cci-25-00218
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88