Automated Extraction of Patient-Centered Outcomes After Breast Cancer Treatment: An Open-Source Large Language Model–Based Toolkit
Our discovery indicates that although LLMs serve as effective general-purpose models for tasks across various domains, they require fine-tuning when applied to the clinician domain. Our proposed approach has the potential to lead more efficient, adaptable models for PCO information extraction, reducing reliance on extensive computational resources while still delivering superior performance for specific tasks.
Authors
- Daniel L. Rubin (ORCID: https://orcid.org/0000-0001-5057-4369)
- Imon Banerjee (ORCID: https://orcid.org/0000-0002-3327-8004)
- Kevin C. Ward (ORCID: https://orcid.org/0000-0001-5893-6192)
- Shubham Trivedi
- Allison W. Kurian (ORCID: https://orcid.org/0000-0002-6175-9470)
- Theresa H.M. Keegan (ORCID: https://orcid.org/0000-0002-1961-4008)
- Man Luo (ORCID: https://orcid.org/0009-0001-0067-2283)
Institutions
- Emory University (US)
- Mayo Clinic Hospital (US)
- Arizona State University (US)
- University of California, Davis (US)
- Stanford University (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-08-01
- DOI
- https://doi.org/10.1200/cci.23.00258
- Citations
- 9
- Primary Topic
- Cancer survivorship and care
- Type
- article
- Field-Weighted Citation Impact
- 2.54
Funders
- Breast Cancer Research Foundation
- National Institutes of Health
- National Cancer Institute
- Stanford Cancer Institute