Extraction of Treatments and Responses From Non–Small Cell Lung Cancer Clinical Notes Using Natural Language Processing
The proposed NLP system can reliably extract structured treatment and response information from unstructured NSCLC oncology notes with high accuracy. This automated approach can assist in abstracting critical cancer treatment outcomes from clinical narrative text, thereby streamlining real-world data analysis and supporting the generation of RWE in oncology.
Authors
- Subhash Edupuganti (ORCID: https://orcid.org/0000-0003-0964-9271)
- Devin Dressman
- Roby Thomas (ORCID: https://orcid.org/0000-0003-4828-4586)
- Manisha Bhattacharya
- P. Yang
- Yang Ren (ORCID: https://orcid.org/0000-0002-6128-5826)
- Sonish Sivarajkumar (ORCID: https://orcid.org/0000-0003-4173-1988)
- David Lazris
- Michael J. Davis (ORCID: https://orcid.org/0000-0002-7992-9300)
- Yan Hu (ORCID: https://orcid.org/0000-0001-8723-9900)
- Yufei Huang
- Yanshan Wang
- Hua Xu
Institutions
- University of Pittsburgh (US)
- Yale University (US)
- Mayo Clinic in Arizona (US)
- UPMC Hillman Cancer Center (US)
- University of Pittsburgh Medical Center (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1200/cci-25-00138
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00