Semi‐automated topic identification for radiation oncology safety event reports using natural language processing and statistical modeling
We demonstrated a semi-automated method to categorize Radiation Oncology safety-related event reports by topic, offering an expedited alternative to a person reading many reports and enabling the identification of topics not discovered by reading individual reports.
Authors
- Qian Sophia Zhang
- Patricia A. Sponseller
- John Kang (ORCID: https://orcid.org/0000-0003-3915-8350)
- M. Glenn (ORCID: https://orcid.org/0000-0001-6780-4458)
- Kevin Lybarger (ORCID: https://orcid.org/0000-0001-5798-2664)
- K. Blau (ORCID: https://orcid.org/0009-0006-7193-2771)
- Eric Ford (ORCID: https://orcid.org/0000-0002-5170-1726)
Institutions
- Northwestern University (US)
- The University of Texas MD Anderson Cancer Center (US)
- George Mason University (US)
- Fred Hutch Cancer Center (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/mp.17936
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00