Automated Knowledge Modeling for Cancer Clinical Practice Guidelines

TL;DR

This paper introduces an automated approach to extract and structure knowledge from cancer clinical practice guidelines, enabling better programmatic access and updates for rapidly evolving medical recommendations.

Contribution

It presents a novel automated method for extracting and modeling cancer guidelines, incorporating enrichment strategies and machine learning for improved knowledge representation.

Findings

Successful extraction and modeling of NCCN cancer guidelines

Achieved 81% accuracy in node classification using SVM

Enhanced guideline models enable programmatic querying

Abstract

Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this developing knowledge. A knowledge model of the guidelines document suitable for programmatic interaction is required. This work proposes an automated method for extraction of knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology and generating a structured model containing the retrieved knowledge. The proposed method was tested using two versions of NCCN Non-Small Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful extraction and modeling of knowledge. Three enrichment strategies using Cancer staging information, Unified Medical Language System (UMLS) Metathesaurus & National Cancer Institute thesaurus (NCIt) concepts, and Node classification are also presented to enhance the model towards enabling programmatic traversal and querying of cancer care guidelines. The Node classification was performed using a Support Vector Machine (SVM) model, achieving a classification accuracy of 0.81 with 10-fold cross-validation.