Development of a species-specific polymerase chain reaction assay for Gardnerella vaginalis

The nucleotide sequence of the region between the 16S and 23S rRNA genes of the facultative anaerobic bacteriumGardnerella vaginalishas been determined, together with the 5′ proximal 500 nucleotides of the 23S rRNA gene. Regions suited for the development of specific, probe-confirmable polymerase chain reaction (PCR) assays were selected. PCR assays were evaluated with respect to sensitivity and specificity, the latter in comparison with a number ofG. vaginalisreference strains and closely related species likeBifidobacteriumspp. In an initial diagnostic study it appeared that the PCR test detectedG. vaginalisin 40% of women irrespective of their clinical status. Ten out of 11 patients suffering from bacterial vaginosis as defined on the basis of clinical parameters were carryingG. vaginalis

Evaluating Trauma Patients: Addressing Missing Covariates with Joint Optimization

Missing values are a common problem when applying classification algorithms to real-world medical data. This is especially true for trauma patients, where the emergent nature of the cases makes it difficult to collect all of the relevant data for each patient. Standard methods for handling missingness first learn a model to estimate missing data values, and subsequently train and evaluate a classifier using data imputed with this model. Recently, several proposed methods have demonstrated the benefits of jointly estimating the imputation model and classifier parameters. However, these methods make assumptions that limit their utility with many real-world medical datasets. For example, the assumption that data elements are missing at random is often invalid. We address this situation by exploring a novel approach for jointly learning the imputation model and classifier. Unlike previous algorithms, our approach makes no assumptions about the missingness of the data, can be used with arbitrary probabilistic data models and classification loss functions, and can be used when both the training and testing data have missing values. We investigate the utility of this approach on the prediction of several patient outcomes in a large national registry of trauma patients, and find that it significantly outperforms standard sequential methods

Automatic Extraction of Concepts to Extend RadLex

RadLex™, the Radiology Lexicon, is a controlled vocabulary of terms used in radiology. It was developed by the Radiological Society of North America in recognition of a lack of coverage of these radiology concepts by other lexicons. There are still additional concepts, particularly those related to imaging observations and imaging observation characteristics, that could be added to the lexicon. We used a free and open source software system to extract these terms from the medical literature. The system retrieved relevant articles from the PubMed repository and passed them through modules in the Apache Unstructured Information Management Architecture. Image observations and image observation characteristics were identified through a seven-step process. The system was run on a corpus of 1,128 journal articles. The system generated lists of 624 imaging observations and 444 imaging observation characteristics. Three domain experts evaluated the top 100 terms in each list and determined a precision of 52% and 26%, respectively, for identification of image observations and image observation characteristics. We conclude that candidate terms for inclusion in standardized lexicons may be extracted automatically from the peer-reviewed literature. These terms can then be reviewed for curation into the lexicon