Medical concept extraction: A comparison of statistical and semantic methods

The goal of medical concept extraction is to identify phrases that refer to medical concepts of interest such as problems, treatments and tests from medical documents. In this study, three types of medical concept extraction models are developed and then compared them. The first concept extraction t...

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Bibliographic Details
Published in2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) pp. 35 - 38
Main Authors Pyae Khin, Nyein Pyae, Lynn, Khin Thidar
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2017
Subjects
CRF
Data mining
Feature extraction
Hidden Markov models
Machine Learning
Medical Concept Extraction
Medical diagnostic imaging
MetaMap
Natural language processing
NLP
Semantics
Unified modeling language
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Summary:The goal of medical concept extraction is to identify phrases that refer to medical concepts of interest such as problems, treatments and tests from medical documents. In this study, three types of medical concept extraction models are developed and then compared them. The first concept extraction task is mainly based upon semantic features obtained from a domain-knowledge based method using MetaMap, and the other two are machine-learning methods with using sequential classifier Conditional Random Fields (CRF) for both with and without MetaMap outputs as features. Among the three concept extraction models, the combined approach of CRF with MetaMap features obtained the best results.
DOI:10.1109/SNPD.2017.8022697