Conference proceeding
Interpretable Cancer Staging Using Knowledge Elicitation in Large Language Models
Proceedings (IEEE International Conference on Healthcare Informatics. Online), pp 654-654
18 Jun 2025
Abstract
Cancer staging is critical for patient prognosis and treatment planning, yet extracting pathologic TNM staging from unstructured pathology reports is challenging. In this study, we introduce two LLM-based knowledge elicitation methods-one using a Long-Term Memory approach (KEwLTM) and another employing Retrieval-Augmented Generation (KEwRAG)-to enable large language models (LLMs) to induce domain-specific rules for cancer staging.
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Details
- Title
- Interpretable Cancer Staging Using Knowledge Elicitation in Large Language Models
- Creators
- Yeawon Lee - Drexel University
- Publication Details
- Proceedings (IEEE International Conference on Healthcare Informatics. Online), pp 654-654
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001661529600071
- Scopus ID
- 2-s2.0-105012717499
- Other Identifier
- 991022197429904721