# Decision Knowledge Graphs: Construction of and Usage in Question Answering for Clinical Practice Guidelines

arXiv: https://arxiv.org/abs/2308.02984 · HTML: https://mlsanity.com/paper/2308.02984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs](https://mlsanity.com/paper/2501.13984/) (2025)

## Abstract (arXiv)

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 past treatments, and are updated frequently. A doctor treating a particular patient can use these CPGs to know how past patients with similar conditions were treated successfully and can find the recommended treatment procedure. In this paper, we present a Decision Knowledge Graph (DKG) representation to store CPGs and to perform question-answering on CPGs. CPGs are very complex and no existing representation is suitable to perform question-answering and searching tasks on CPGs. As a result, doctors and practitioners have to manually wade through the guidelines, which is inefficient. Representation of CPGs is challenging mainly due to frequent updates on CPGs and decision-based structure. Our proposed DKG has a decision dimension added to a Knowledge Graph (KG) structure, purported to take care of decision based behavior of CPGs. Using this DKG has shown 40% increase in accuracy compared to fine-tuned BioBert model in performing question-answering on CPGs. To the best of our knowledge, ours is the first attempt at creating DKGs and using them for representing CPGs.