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In the medical domain, several disease treatment procedures have beendocumented properly as a set of instructions known as Clinical PracticeGuidelines (CPGs). CPGs have been developed over the years on the basis of pasttreatments, and are updated frequently. A doctor treating a particular patientcan use these CPGs to know how past patients with similar conditions weretreated successfully and can find the recommended treatment procedure. In thispaper, we present a Decision Knowledge Graph (DKG) representation to store CPGsand to perform question-answering on CPGs. CPGs are very complex and noexisting representation is suitable to perform question-answering and searchingtasks on CPGs. As a result, doctors and practitioners have to manually wadethrough the guidelines, which is inefficient. Representation of CPGs ischallenging mainly due to frequent updates on CPGs and decision-basedstructure. 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-tunedBioBert model in performing question-answering on CPGs. To the best of ourknowledge, ours is the first attempt at creating DKGs and using them forrepresenting CPGs.

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