Validation of Non–Small Cell Lung Cancer Clinical Insights Using a Generalized Oncology Natural Language Processing Model
This study validates that Optum's oncology NLP model has high precision and recall with clinical real-world data and is a reliable model to support research studies and clinical trials. This validation study shows that our nonspecific solid tumor and blood cancer oncology model is generalizable to successfully extract clinical information from specific cancer cohorts.
Authors
- Anne-Marie Currie
- Stacey DaCosta Byfield (ORCID: https://orcid.org/0000-0001-8923-0643)
- Rachel Kenney (ORCID: https://orcid.org/0000-0003-1311-5520)
- Lorre Ochs
- John Liu (ORCID: https://orcid.org/0000-0003-3288-275X)
- Clara Gagnon
- Xiaoren Chen
- K Shintani
Institutions
- Optum (United States) (US)
- New York University (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1200/cci.23.00099
- Citations
- 1
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.35