Studies in health technology and informatics, 160(Pt 2):1025–1029, 2010. Paper abstract bibtex
BACKGROUND: Adverse Drug Events (ADEs) endanger the patients. Their detection and prevention is essential to improve the patients' safety. In the absence of computerized physician order entry (CPOE), discharge summaries are the only source of information about the drugs prescribed during a hospitalization. The French Multierminology Indexer (F-MTI) can help to extract drug-related information from those records. METHODS: In first and second validation steps, the performance of the F-MTI tool is evaluated to extract ICD10 and ATC codes from free-text documents. In third step, potential ADE detection rules are used and the confidences of those rules are compared in several hospitals: using a CPOE vs. using semantic mining of free-text documents, diagnoses and lab results being available in both cases. RESULTS: The F-MTI tool is able to extract ATC codes from documents. Moreover, the evaluation shows coherent and comparable results between the hospitals with CPOEs and the hospital with drugs information extracted from the reports for potential ADE detection. CONCLUSION: semantic mining using F-MTI can help to identify previous cases of potential ADEs in absence of CPOE.