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Related Experiment Video

Updated: Jul 8, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Automated Knowledge Modeling for Cancer Clinical Practice Guidelines.

Pralaypati Ta, Bhumika Gupta, Arihant Jain

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    This study introduces an automated method to extract knowledge from National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines (CPGs) for oncology, creating a structured model for better cancer care guideline management.

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    Area of Science:

    • Oncology
    • Medical Informatics
    • Computational Biology

    Background:

    • Clinical Practice Guidelines (CPGs) for cancer rapidly evolve with new research, but current document formats hinder knowledge management.
    • A programmatic approach is needed to handle the dynamic nature of cancer CPGs.

    Purpose of the Study:

    • To develop an automated method for extracting knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology.
    • To generate a structured knowledge model from extracted CPG data for programmatic interaction.
    • To enhance the model with cancer staging, terminologies (UMLS, NCIt), and node classification for improved querying.

    Main Methods:

    • Developed an automated knowledge extraction pipeline for NCCN CPGs.
    • Tested the method on Non-Small Cell Lung Cancer (NSCLC) CPGs across two versions.
    • Implemented enrichment strategies including cancer staging, UMLS/NCIt concept mapping, and Support Vector Machine (SVM) node classification.

    Main Results:

    • Successfully extracted and modeled knowledge from NCCN NSCLC CPGs.
    • Demonstrated faithful knowledge extraction and modeling capabilities.
    • Achieved 0.81 accuracy in node classification using an SVM model with 10-fold cross-validation.

    Conclusions:

    • The proposed automated method effectively extracts and models knowledge from NCCN CPGs.
    • Enrichment strategies enhance the model for programmatic traversal and querying of cancer care guidelines.
    • This approach facilitates better management and utilization of evolving cancer CPGs.