Annotation methods to develop and evaluate an expert system based on natural language processing in electronic medical records.

The objective of the SYNODOS collaborative project was to develop a generic IT solution, combining a medical terminology server, a semantic analyser and a knowledge base. The goal of the project was to generate meaningful epidemiological data for various medical domains from the textual content of French medical records. In the context of this project, we built a care pathway oriented conceptual model and corresponding annotation method to develop and evaluate an expert system's knowledge base.

Evaluation of syndromic algorithms for detecting patients with potentially transmissible infectious diseases based on computerised emergency-department data.

Background: The objective of this study was to ascertain the performance of syndromic algorithms for the early detection of patients in healthcare facilities who have potentially transmissible infectious diseases, using computerised emergency department (ED) data.

Methods: A retrospective cohort in an 810-bed University of Lyon hospital in France was analysed. Adults who were admitted to the ED and hospitalised between June 1, 2007, and March 31, 2010 were included (N=10895).

Evaluation of natural language processing from emergency department computerized medical records for intra-hospital syndromic surveillance.

Background: The identification of patients who pose an epidemic hazard when they are admitted to a health facility plays a role in preventing the risk of hospital acquired infection. An automated clinical decision support system to detect suspected cases, based on the principle of syndromic surveillance, is being developed at the University of Lyon's Hôpital de la Croix-Rousse. This tool will analyse structured data and narrative reports from computerized emergency department (ED) medical records.

Evaluation of a French medical multi-terminology indexer for the manual annotation of natural language medical reports of healthcare-associated infections.

Background: Surveillance of healthcare-associated infections is essential to prevention. A new collaborative project, namely ALADIN, was launched in January 2009 and aims to develop an automated detection tool based on natural language processing of medical documents.

Objective: The objective of this study was to evaluate the annotation of natural language medical reports of healthcare-associated infections.