Linguistically Motivated Negation Processing: An Application for the Detection of Risk Indicators in Unstructured Discharge Summaries

 

Caroline Hagege

 

XRCE – Xerox Research Centre Europe, 6 Chemin de Maupertuis, 38240 Meylan, France. (e–mail: Caroline.Hagege@xrce.xerox.com).

 

Manuscript received October 21, 2010.
Manuscript accepted for publication January 19, 2011.

 

Abstract

The paper proposes a linguistically motivated approach to deal with negation in the context of information extraction. This approach is used in a practical application: the automatic detection of cases of hospital acquired infections (HAI) by processing unstructured medical discharge summaries. One of the important processing steps is the extraction of specific terms expressing risk indicators that can lead to the conclusion of HAI cases. This term extraction has to be very accurate and negation has to be taken into account in order to really understand if a string corresponding to a potential risk indicator is attested positively or negatively in the document. We propose a linguistically motivated approach for dealing with negation using both syntactic and semantic information. This approach is first described and then evaluated in the context of our application in the medical domain. The results of evaluation are also compared with other related approaches dealing with negation in medical texts.

Key words: Negation detection, discharge summaries, dependency parsing.

 

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