This paper presents a novel approach to the task of automatically inferring the most probable diagnosis from a given clinical narrative. Structured Knowledge Bases (KBs) can be useful for such complex tasks but not sufficient. Hence, we leverage a vast amount of unstructured free text to integrate with structured KBs. The key innovative ideas include building a concept graph from both structured and unstructured knowledge sources and ranking the diagnosis concepts using the enhanced word embedding vectors learned from integrated sources. Experiments on the TREC CDS and HumanDx datasets showed that our methods improved the results of clinical diagnosis inference.
In the diagnosis context, a system was proposed to automatically infer the most probable diagnosis from clinical narratives REF .
Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
Computer Science