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Authors: Veronica Dahl 1 ; Sara Saghaei 2 and Oliver Schulte 2

Affiliations: 1 Simon Fraser University and Universidad Rovira i Virgili, Canada ; 2 Simon Fraser University, Canada

Keyword(s): medical text, natural language processing, de-identification, hybrid methods, hypothetical reasoning, molecular biology, knowledge extraction, incomplete types, domain taxonomies

Abstract: De-identification is the process of automatic removal of all Private Health Information (PHI) from medical records. The main focus in this active and important research area is on semi-structured records. This narrow focus has allowed the development of standard criteria that formally determines the boundaries of privacy and can be used for evaluations. However, medical records include, as well as semi-structured data from filling in forms, etc., free text in which identifiers are more difficult to detect. In this article we address the problem of de-identification within unstructured medical records.We show how through the followingmethods we are able to recognize, in some cases, identifiers that currently go undetected: (1) Parsing free-form medical text into typed logical relationships including assumptions for candidate identifiers. (2) A novel use of the state-of-the-art engines for processing English queries to the web. A formal definition of our approach within a rigorous logi cal system that supports the implementation of our ideas, is also available on the website. (More)

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Paper citation in several formats:
Dahl, V.; Saghaei, S. and Schulte, O. (2011). Parsing Medical Text into De-identified Databases. In Proceedings of the 1st International Workshop on AI Methods for Interdisciplinary Research in Language and Biology - BILC, (ICAART 2011) ISBN 978-989-8425-42-3, pages 77-87. DOI: 10.5220/0003309700770087

@conference{bilc11,
author={Veronica Dahl. and Sara Saghaei. and Oliver Schulte.},
title={Parsing Medical Text into De-identified Databases},
booktitle={Proceedings of the 1st International Workshop on AI Methods for Interdisciplinary Research in Language and Biology - BILC, (ICAART 2011)},
year={2011},
pages={77-87},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003309700770087},
isbn={978-989-8425-42-3},
}

TY - CONF

JO - Proceedings of the 1st International Workshop on AI Methods for Interdisciplinary Research in Language and Biology - BILC, (ICAART 2011)
TI - Parsing Medical Text into De-identified Databases
SN - 978-989-8425-42-3
AU - Dahl, V.
AU - Saghaei, S.
AU - Schulte, O.
PY - 2011
SP - 77
EP - 87
DO - 10.5220/0003309700770087

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