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

Background: 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. 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 1...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Frank P. Y. Lin, Stephen Anthony, Thomas M. Polasek, Guy Tsafnat, Matthew P. Doogue
• Keywords: Biometrics, BMCBI 2011, Narrow Therapeutic Index, Operating Characteristic Curve, Pharmacokinetic Interaction