Linguistic and temporal processing for discovering hospital acquired infection from patient records

Authors:
Caroline Hagège;Pierre Marchal;Quentin Gicquel;Stefan Darmoni;Suzanne Pereira;Marie-Hélène Metzger
Affiliations:
Xerox Research Centre Europe, Meylan, France;Xerox Research Centre Europe, Meylan, France;UCBL, Lyon, France;CISMEF, Rouen, France;VIDAL, Paris, France;UCBL, Lyon, France
Venue:
KR4HC'10 Proceedings of the ECAI 2010 conference on Knowledge representation for health-care
Year:
2010

Citing 8
Cited 0

Towards a general theory of action and time

Artificial Intelligence
Robustness beyond shallowness: incremental deep parsing

Natural Language Engineering
Annotating and measuring temporal relations in texts

COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Methodological Review: Temporal reasoning with medical data-A review with emphasis on medical natural language processing

Journal of Biomedical Informatics
A metalearning approach to processing the scope of negation

CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports

Journal of Biomedical Informatics
XTM: a robust temporal text processor

CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Natural language processing to detect risk patterns related to hospital acquired infections

WBIE '09 Proceedings of the Workshop on Biomedical Information Extraction

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes the first steps of development of a rulebased system that automatically processes medical records in order to discover possible cases of hospital acquired infections (HAI). The system takes as input a set of patient records in electronic format and gives as output, for each document, information regarding HAI. In order to achieve this goal, a temporal processing together with a deep syntactic and semantic analysis of the patient records is performed. Medical knowledge used by the rules is derived from a set of documents that have been annotated by medical doctors. After a brief description of the context of this work, we present the general architecture of our document processing chain and explain how we perform our temporal and linguistic analysis. Finally, we report our preliminary results and we lay out the next steps of the project.