Evaluating a dictionary of human phenotype terms focusing on rare diseases

Simon Kocbek, Toyofumi Fujiwara, Jin-Dong Kim, Toshihisa Takagi, Tudor Groza


Abstract
Annotating medical text such as clinical notes with human phenotype descriptors is an important task that can, for example, assist in building patient profiles. To automatically annotate text one usually needs a dictionary of predefined terms. However, do to the variety of human expressiveness, current state-of-the art phenotype concept recognizers and automatic annotators struggle with specific domain issues and challenges. In this paper we present results of an-notating gold standard corpus with a dictionary containing lexical variants for the Human Phenotype Ontology terms. The main purpose of the dictionary is to improve the recall of phenotype concept recognition systems. We compare the method with four other approaches and present results.
Anthology ID:
W16-4712
Volume:
Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
CompuTerm | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
104–109
Language:
URL:
https://www.aclweb.org/anthology/W16-4712
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-4712.pdf