# Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs

arXiv: https://arxiv.org/abs/2501.13984 · HTML: https://mlsanity.com/paper/2501.13984/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering](https://mlsanity.com/paper/2308.12060/) (2023)
- [GPT-3.5, GPT-4, or BARD? Evaluating LLMs Reasoning Ability in Zero-Shot Setting and Performance Boosting Through Prompts](https://mlsanity.com/paper/2305.12477/) (2023)
- [Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models](https://mlsanity.com/paper/2403.01972/) (2024)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Few-shot Adaptation Works with UnpredicTable Data](https://mlsanity.com/paper/2208.01009/) (2022)
- [Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records](https://mlsanity.com/paper/2203.06918/) (2022)
- [Automated Knowledge Modeling for Cancer Clinical Practice Guidelines](https://mlsanity.com/paper/2307.10231/) (2023)
- [AI Driven Knowledge Extraction from Clinical Practice Guidelines: Turning Research into Practice](https://mlsanity.com/paper/2012.05489/) (2020)
- [Decision Knowledge Graphs: Construction of and Usage in Question Answering for Clinical Practice Guidelines](https://mlsanity.com/paper/2308.02984/) (2023)

## Built on by (most cited first, 2 of 2)

- [Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data](https://mlsanity.com/paper/2506.14704/) (2025)
- [PathWISE: Multi-Agent Cancer Pathway Triaging Ontology Learning from Clinical Flowcharts](https://mlsanity.com/paper/2605.25970/) (2026)

## Abstract (arXiv)

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 medical professionals in the treatment decision process, it is necessary to fully capture the guideline knowledge, particularly the contexts and their relationships in the graph. While several existing works have utilized these guidelines to create rule bases for Clinical Decision Support Systems, limited work has been done toward directly capturing the full medical knowledge contained in CPGs. This work proposes an approach to create a contextually enriched, faithful digital representation of National Comprehensive Cancer Network (NCCN) Cancer CPGs in the form of graphs using automated extraction and node & relationship classification. We also implement semantic enrichment of the model by using Large Language Models (LLMs) for node classification, achieving an accuracy of 80.86% and 88.47% with zero-shot learning and few-shot learning, respectively. Additionally, we introduce a methodology for answering natural language questions with constraints to guideline text by leveraging LLMs to extract the relevant subgraph from the guideline knowledge base. By generating natural language answers based on subgraph paths and semantic information, we mitigate the risk of incorrect answers and hallucination associated with LLMs, ensuring factual accuracy in medical domain Question Answering.