Medical semantic similarity with a neural language model

De Vine, Lance, Zuccon, Guido, Koopman, Bevan, Sitbon, Laurianne, & Bruza, Peter (2014) Medical semantic similarity with a neural language model. In Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, ACM, Shanghai, China, pp. 1819-1822.

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Abstract

Advances in neural network language models have demonstrated that these models can effectively learn representations of words meaning. In this paper, we explore a variation of neural language models that can learn on concepts taken from structured ontologies and extracted from free-text, rather than directly from terms in free-text.

This model is employed for the task of measuring semantic similarity between medical concepts, a task that is central to a number of techniques in medical informatics and information retrieval. The model is built with two medical corpora (journal abstracts and patient records) and empirically validated on two ground-truth datasets of human-judged concept pairs assessed by medical professionals. Empirically, our approach correlates closely with expert human assessors ($\approx$ 0.9) and outperforms a number of state-of-the-art benchmarks for medical semantic similarity.

The demonstrated superiority of this model for providing an effective semantic similarity measure is promising in that this may translate into effectiveness gains for techniques in medical information retrieval and medical informatics (e.g., query expansion and literature-based discovery).

Impact and interest:

3 citations in Scopus
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ID Code: 83704
Item Type: Conference Paper
Refereed: Yes
Keywords: neural network, ontology, medical concept, language model
DOI: 10.1145/2661829.2661974
ISBN: 9781450325981
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Organisation of Information and Knowledge Resources (080707)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > CLINICAL SCIENCES (110300)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 ACM
Deposited On: 20 Apr 2015 22:48
Last Modified: 30 Apr 2015 19:38

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