Knowledge Models for Cancer Clinical Practice Guidelines : Construction, Management and Usage in Question Answering

TL;DR

This paper presents an improved automated knowledge modeling algorithm for cancer clinical practice guidelines, enabling better construction, comparison, and question-answering capabilities across different cancer types and guideline versions.

Contribution

The work introduces a novel automated algorithm for creating and comparing knowledge models from NCCN cancer guidelines, enhancing question-answering accuracy and update detection.

Findings

Achieved 54.5% answer accuracy from treatment algorithms.

Achieved 81.8% answer accuracy from guideline discussion parts.

Successfully modeled multiple cancer types with minimal human intervention.

Abstract

An automated knowledge modeling algorithm for Cancer Clinical Practice Guidelines (CPGs) extracts the knowledge contained in the CPG documents and transforms it into a programmatically interactable, easy-to-update structured model with minimal human intervention. The existing automated algorithms have minimal scope and cannot handle the varying complexity of the knowledge content in the CPGs for different cancer types. This work proposes an improved automated knowledge modeling algorithm to create knowledge models from the National Comprehensive Cancer Network (NCCN) CPGs in Oncology for different cancer types. The proposed algorithm has been evaluated with NCCN CPGs for four different cancer types. We also proposed an algorithm to compare the knowledge models for different versions of a guideline to discover the specific changes introduced in the treatment protocol of a new version. We created a question-answering (Q&A) framework with the guideline knowledge models as the augmented knowledge base to study our ability to query the knowledge models. We compiled a set of 32 question-answer pairs derived from two reliable data sources for the treatment of Non-Small Cell Lung Cancer (NSCLC) to evaluate the Q&A framework. The framework was evaluated against the question-answer pairs from one data source, and it can generate the answers with 54.5% accuracy from the treatment algorithm and 81.8% accuracy from the discussion part of the NCCN NSCLC guideline knowledge model.