Accuracy of using natural language processing methods for identifying healthcare-associated infections.

Journal: International journal of medical informatics

Published Date: Sep 1, 2018

Abstract

OBJECTIVE: There is a growing interest in using natural language processing (NLP) for healthcare-associated infections (HAIs) monitoring. A French project consortium, SYNODOS, developed a NLP solution for detecting medical events in electronic medical records for epidemiological purposes. The objective of this study was to evaluate the performance of the SYNODOS data processing chain for detecting HAIs in clinical documents.

Authors

Nastassia Tvardik

Université Lyon 1, UMR CNRS UCBL 5558, Lyon, France.
Ivan Kergourlay

Department of Biomedical Informatics, Rouen University Hospital, TIBS, LITIS EA 4108 Rouen University, France.
André Bittar

Holmes Semantic Solutions, Grenoble, France.
Frédérique Segond

Viseo Technologies, Grenoble, France.
Stefan Darmoni

Department of Biomedical Informatics, Rouen University Hospital, TIBS, LITIS EA 4108 Rouen University, France.
Marie-Hélène Metzger

Université Lyon 1, UMR CNRS UCBL 5558, Lyon, France.

Keywords

Adult Algorithms Cross Infection Electronic Health Records Hospitals, University Humans Intensive Care Units Natural Language Processing Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (30032970)

Accuracy of using natural language processing methods for identifying healthcare-associated infections.

Abstract

Authors

Keywords

External Resources

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