Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge

Yuan Ling, Yuan An, Sadid Hasan


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.
Anthology ID:
W17-1904
Volume:
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Jose Camacho-Collados, Mohammad Taher Pilehvar
Venue:
SENSE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
31–36
Language:
URL:
https://aclanthology.org/W17-1904
DOI:
10.18653/v1/W17-1904
Bibkey:
Cite (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.
Cite (Informal):
Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge (Ling et al., SENSE 2017)
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PDF:
https://aclanthology.org/W17-1904.pdf