Medical pedagogical resources management.

The main objective of this work is to help the management of training resources for students using a pedagogical network available at the Medical School of Rennes. With the increase of the number of connections and the number of medical documents available on this network, the management of new contents requires a lot of efforts for the webmaster. In order to improve the management of the resources, we implemented an automatic web engine for teachers, able to manage the links for the most interesting resources for their practice.

Automatic concept extraction from spoken medical reports.

OBJECTIVE: The objective of this project is to investigate methods whereby a combination of speech recognition and automated indexing methods substitute for current transcription and indexing practices. METHODS: We based our study on existing speech recognition software programs and on NOMINDEX, a tool that extracts MeSH concepts from medical text in natural language and that is mainly based on a French medical lexicon and on the UMLS. For each document, the process consists of three steps: (1) dictation and digital audio recording, (2) speech recognition, (3) automatic indexing. The evaluation consisted of a comparison between the set of concepts extracted by NOMINDEX after the speech recognition phase and the set of keywords manually extracted from the initial document. The method was evaluated on a set of 28 patient discharge summaries extracted from the MENELAS corpus in French, corresponding to in-patients admitted for coronarography. RESULTS: The overall precision was 73% and the overall recall was 90%. Indexing errors were mainly due to word sense ambiguity and abbreviations. A specific issue was the fact that the standard French translation of MeSH terms lacks diacritics. A preliminary evaluation of speech recognition tools showed that the rate of accurate recognition was higher than 98%. Only 3% of the indexing errors were generated by inadequate speech recognition. DISCUSSION: We discuss several areas to focus on to improve this prototype. However, the very low rate of indexing errors due to speech recognition errors highlights the potential benefits of combining speech recognition techniques and automatic indexing.

Combining voice recognition and automatic indexing of medical reports.

Medical records have been evolving from the traditional paper-based records to digital ones, from the method of dictating reports and transcription to voice recognition systems. The transition to digital operations will not be complete until we have the ability to combine voice recognition with automated indexing of texts. This paper introduces the methods we used to evaluate existing voice recognition software programs and presents NOMINDEX, a system that turns a medical text into MeSH codes, using the French ADM lexical database. Those systems were applied to 28 patient discharge summaries in French, produced after a coronarography, and extracted from the MENELAS corpus of texts. Using the best configuration for voice recognition, the rate of accurate recognition exceeds 98 percent. Among the indexing concepts assigned by NOMINDEX, 25 percent were not pertinent and 12 percent of the relevant concepts were missing. Most errors were related to confusion between common language and medical language, and to the coverage of the ADM lexical database. Best results would be expected with a more comprehensive lexical resource In addition, only 3 percent of the errors generated by inadequate voice recognition that remained in the configuration that performed better, impacted on automatic indexing by NOMINDEX.

Automatic conceptual indexing of French pharmaceutical theses.

French pharmaceutical theses are rarely quoted. If the main obstacles originate from language or access barriers, proper indexation could also be blamed. Manually extracted key-words don't necessary come from a structured thesaurus. In the following work, this manual indexing method is compared to an automated one, "Nomindex", based on UMLS. The automated method is improved by the addition of a relevance scoring system. The first indexing step consists of downloading, adapting and indexing theses in electronic format. Results will then be analyzed and sorted by relevance, through the comparison of classic statistical indices (noise, silence and relevance). It was assumed that the manually obtained key-words were always relevant. The silence of manual indexing is nevertheless high: seven new key-words are proposed by Nomindex, which results are mixed (10% of silence, but 50% of noise). These results are promising on the first experiment on pharmaceutical document without lexicon improvement. The indexing, if it is currently insufficient for a real life use, could easily be improved by specific updates of the lexicon.