Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France. - Université de Rennes Access content directly
Journal Articles Studies in Health Technology and Informatics Year : 2012

Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France.

Abstract

The surveillance of Surgical Site Infections (SSI) contributes to the management of risk in French hospitals. Manual identification of infections is costly, time-consuming and limits the promotion of preventive procedures by the dedicated teams. The introduction of alternative methods using automated detection strategies is promising to improve this surveillance. The present study describes an automated detection strategy for SSI in neurosurgery, based on textual analysis of medical reports stored in a clinical data warehouse. The method consists firstly, of enrichment and concept extraction from full-text reports using NOMINDEX, and secondly, text similarity measurement using a vector space model. The text detection was compared to the conventional strategy based on self-declaration and to the automated detection using the diagnosis-related group database. The text-mining approach showed the best detection accuracy, with recall and precision equal to 92% and 40% respectively, and confirmed the interest of reusing full-text medical reports to perform automated detection of SSI.
Fichier principal
Vignette du fichier
Full-text Automated Detection-SHTI192-0572.pdf (142.89 Ko) Télécharger le fichier
Origin : Publication funded by an institution
Loading...

Dates and versions

hal-01142182 , version 1 (14-04-2015)

Licence

Attribution - NonCommercial

Identifiers

Cite

Boris Campillo-Gimenez, Nicolas Garcelon, Pascal Jarno, Jean Marc Chapplain, Marc Cuggia. Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France.. Studies in Health Technology and Informatics, 2012, 192, pp.572-5. ⟨10.3233/978-1-61499-289-9-572⟩. ⟨hal-01142182⟩

Collections

INSERM UNIV-RENNES
110 View
181 Download

Altmetric

Share

Gmail Facebook X LinkedIn More