Disease Identification in Electronic Health Records - An Ontology based Approach

Ioana Barbantan, Camelia Lemnaru, Rodica Potolea

Abstract

Exploiting efficiently medical data from Electronic Health Records (EHRs) is a current joint research focus of the knowledge extraction and the medical communities. EHR structuring is essential for the efficient exploitation of the information they capture. To that end, concept identification and categorization represent key tasks. This paper presents a disease identification approach which applies several NLP document pre-processing steps, queries the SNOMED-CT ontology and then applies a filtering rule on the retrieved information. The hierarchical approach provides a better filtering of the concepts, reducing the amount of falsely identified disease concepts. We have performed a series of evaluations on the Medline abstracts dataset. The results obtained so far are promising – our method achieves a precision of 87.79% and a recall of 87.12%, better than the results obtained by Apache’s cTAKES system on the same task and dataset.

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Paper Citation


in Harvard Style

Barbantan I., Lemnaru C. and Potolea R. (2014). Disease Identification in Electronic Health Records - An Ontology based Approach . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 261-268. DOI: 10.5220/0005082002610268


in Bibtex Style

@conference{kdir14,
author={Ioana Barbantan and Camelia Lemnaru and Rodica Potolea},
title={Disease Identification in Electronic Health Records - An Ontology based Approach},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={261-268},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005082002610268},
isbn={978-989-758-048-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Disease Identification in Electronic Health Records - An Ontology based Approach
SN - 978-989-758-048-2
AU - Barbantan I.
AU - Lemnaru C.
AU - Potolea R.
PY - 2014
SP - 261
EP - 268
DO - 10.5220/0005082002610268