BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

<p>Abstract</p> <p>Background</p> <p>The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest.</p> <p>Results</p> <p>BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH) and the PharmacoKinetic Interaction Screening (PKIS) database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100%) and 157 (of 197) minor drug classes (80%) with areas under the receiver operating characteristic curve (AUC) > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238) adverse events (73%), up to 12 (of 15) groups of clinically significant cytochrome P450 enzyme (CYP) inducers or inhibitors (80%), and up to 11 (of 14) groups of narrow therapeutic index drugs (79%). Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task.</p> <p>Conclusions</p> <p>BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.</p

Angiosperm fossils in supposed Jurassic volcanogenic shales, Antarctica

During palaeobotanical studies in the Antarctic Peninsula in February 1979, late Cretaceous or younger fossil angiosperm leaves were found within volcaniclastic rocks widely believed to be of late Jurassic age1. Although poorly preserved, these fossils are of great stratigraphical importance. They occur at Cape Alexandra, Adelaide Island (Fig. 1), in rocks correlated with the lowest part of the exposed succession (Sloman Glacier succession1). The fossils were found less than 10 km from the type locality for this succession at the head of Sloman Glacier (Fig. 1). However, towards the northern end of the island at Mount Bouvier (Fig. 1), ammonites and bivalves indicate that supposedly equivalent rocks1 are of Upper Jurassic age2. This intensive study of a very small part of the succession indicates that the volcanic history of Adelaide Island is much more complicated than was previously suggested by reconnaissance mapping

Classroom assessment practices and teacher learning: An Australian perspective

This chapter draws on empirical evidence, to explore the purposes and the approaches to classroom assessment used by some Australian primary and secondary teachers. Insights into how teachers learn in the development of classroom assessment for formative and summative purposes and the strategies they employ to address student learning needs are described and critically analysed. The importance of teacher agency when learning about classroom assessment to enhance validity, consistency and equity is addressed