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        <title>Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge
%A Ling, Yuan
%A An, Yuan
%A Hasan, Sadid
%Y Camacho-Collados, Jose
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F ling-etal-2017-improving
%X 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.
%R 10.18653/v1/W17-1904
%U https://aclanthology.org/W17-1904
%U https://doi.org/10.18653/v1/W17-1904
%P 31-36

Markdown (Informal)

[Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge](https://aclanthology.org/W17-1904) (Ling et al., SENSE 2017)

• Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge (Ling et al., SENSE 2017)

ACL

• Yuan Ling, Yuan An, and Sadid Hasan. 2017. Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge. In Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pages 31–36, Valencia, Spain. Association for Computational Linguistics.