Knowledge Models for Cancer Clinical Practice Guidelines : Construction, Management and Usage in Question Answering
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementQuestion AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Automated Knowledge Modeling for Cancer Clinical Practice Guidelines
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 d…
ClassificationNode ClassificationComprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs
The updated recommendations on diagnostic procedures and treatment pathways for a medical condition are documented as graphical flows in Clinical Practice Guidelines (CPGs). For effective use of the CPGs in helping medic…
DiagnosticFew-Shot LearningHallucinationNode Classification+2CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records
Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their i…
Knowledge GraphsDecision Knowledge Graphs: Construction of and Usage in Question Answering for Clinical Practice Guidelines
In the medical domain, several disease treatment procedures have been documented properly as a set of instructions known as Clinical Practice Guidelines (CPGs). CPGs have been developed over the years on the basis of pas…
Knowledge GraphsQuestion AnsweringCPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support
Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into Artificial Intelligence (AI) remains challenging. Previous approaches, such as rule-based system…