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Conference proceeding
Classifying Cancer Stage with Open-Source Clinical Large Language Models
2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pp 76-82
03 Jun 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Cancer stage classification is important for making treatment and care management plans for oncology patients. Information on staging is often included in unstructured form in clinical, pathology, radiology and other free-text reports in the electronic health record system, requiring extensive work to parse and obtain. To facilitate the extraction of this in-formation, previous NLP approaches rely on labeled training datasets, which are labor-intensive to prepare. In this study, we demonstrate that without any labeled training data, open-source clinical large language models (LLMs) can extract pathologic tumor-node-metastasis (pTNM) staging information from real-world pathology reports. Our experiments compare LLMs and a BERT-based model fine-tuned using the labeled data. Our findings suggest that while LLMs still exhibit subpar performance in Tumor (T) classification, with the appropriate adoption of prompting strategies, they can achieve comparable performance on Metastasis (M) classification and improved performance on Node (N) classification.
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Details
- Title
- Classifying Cancer Stage with Open-Source Clinical Large Language Models
- Creators
- Chia-Hsuan Chang - Drexel UniversityMary M. Lucas - Drexel UniversityGrace Lu-Yao - Thomas Jefferson UniversityChristopher C. Yang - Drexel University
- Publication Details
- 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pp 76-82
- Publisher
- IEEE
- Number of pages
- 7
- Grant note
- IIS-1741306,IIS-2235548 / National Science Foundation (10.13039/100000001) DoD W91XWH-05-1-023 / Department of Defense (10.13039/100000005)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001304501700010
- Scopus ID
- 2-s2.0-85203711227
- Other Identifier
- 991021901302404721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Medical Informatics