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.

翻译：在医学领域，多种疾病治疗流程已被系统整理为被称为“临床实践指南（CPGs）”的指令集。这些CPGs基于既往治疗方案长期积累并持续更新。临床医生在为特定患者制定治疗方案时，可借助CPGs了解如何成功治疗过往相似病例，从而找到推荐的治疗流程。本文提出一种决策知识图谱（DKG）表征方法，用于存储CPGs并实现基于CPGs的问答功能。由于CPGs具有高度复杂性，现有表征方法均无法有效支持CPGs的问答与检索任务，导致医生和从业人员不得不手动查阅指南，效率低下。CPGs的表征主要面临两大挑战：频繁的更新机制与基于决策的结构特性。我们在知识图谱（KG）结构基础上新增决策维度，提出DKG模型，旨在处理CPGs的决策导向行为。实验表明，在CPGs问答任务中，使用DKG较微调后的BioBert模型准确率提升40%。据我们所知，这是首次尝试构建DKG并将其应用于CPGs表征。