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International audience; Background: Most of the institutional and research information in the biomedical domain is available in the formof English text. Even in countries where English is an official language, such as the United States, language can be abarrier for accessing biomedical information for non-native speakers. Recent progress in machine translationsuggests that this technique could help make English texts accessible to speakers of other languages. However, thelack of adequate specialized corpora needed to train statistical models currently limits the quality of automatictranslations in the biomedical domain.Results: We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain,using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINEand abstract text automatically retrieved from journal websites, which substantially extends the corpora used inprevious work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish)we use the Moses package to train a statistical machine translation model that outperforms previous models forautomatic translation of biomedical text.Conclusions: We have built translation data sets in the biomedical domain that can easily be extended to otherlanguages available in MEDLINE. These sets can successfully be applied to train statistical machine translationmodels. While further progress should be made by incorporating out-of-domain corpora and domain-specificlexicons, we believe that this work improves the automatic translation of biomedical texts.

Background The Internet is a major source of health information but most seekers are not familiar with medical vocabularies. Hence, their searches fail due to bad query formulation. Several methods have been proposed to improve information retrieval: query expansion, syntactic and semantic techniques or knowledge-based methods. However, it would be useful to clean those queries which are misspelled. In this paper, we propose a simple yet efficient method in order to correct misspellings of queries submitted by health information seekers to a medical online search tool. Methods In addition to query normalizations and exact phonetic term matching, we tested two approximate string comparators: the similarity score function of Stoilos and the normalized Levenshtein edit distance. We propose here to combine them to increase the number of matched medical terms in French. We first took a sample of query logs to determine the thresholds and processing times. In the second run, at a greater scale we tested different combinations of query normalizations before or after misspelling correction with the retained thresholds in the first run. Results According to the total number of suggestions (around 163, the number of the first sample of queries), at a threshold comparator score of 0.3, the normalized Levenshtein edit distance gave the highest F-Measure (88.15%) and at a threshold comparator score of 0.7, the Stoilos function gave the highest F-Measure (84.31%). By combining Levenshtein and Stoilos, the highest F-Measure (80.28%) is obtained with 0.2 and 0.7 thresholds respectively. However, queries are composed by several terms that may be combination of medical terms. The process of query normalization and segmentation is thus required. The highest F-Measure (64.18%) is obtained when this process is realized before spelling-correction. Conclusions Despite the widely known high performance of the normalized edit distance of Levenshtein, we show in this paper that its combination with the Stoilos algorithm improved the results for misspelling correction of user queries. Accuracy is improved by combining spelling, phoneme-based information and string normalizations and segmentations into medical terms. These encouraging results have enabled the integration of this method into two projects funded by the French National Research Agency-Technologies for Health Care. The first aims to facilitate the coding process of clinical free texts contained in Electronic Health Records and discharge summaries, whereas the second aims at improving information retrieval through Electronic Health Records.