Abstract
BackgroundThe 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.ResultsBICEPP 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.ConclusionsBICEPP 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.
Highlights
The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources
We have developed a novel method, the BInary Characteristics Extractor and biomedical Properties Predictor (BICEPP), to classify properties of drugs and subsequently validated this approach on data collected from traditional analytical methods derived from the knowledge of field experts
We evaluated the performance of BICEPP on many drug characteristics, including therapeutic classes, adverse effects, and their potentials for pharmacokinetic drugdrug interactions
Summary
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. A frequent inquiry in biology and medicine is to ask whether a biomedical entity (e.g., a drug) and a characteristic (e.g., an adverse effect) are associated with each other. Such true-false relationships form the core of scientific hypotheses. As they are crucial to our interpretation of biomedical phenomena, considerable amount of manpower and resources are often spent on their discovery and assimilation. In the systematic organisation of scientific knowledge, text mining methods have been shown to be effective compared to the manual curation of pharmacogenetic databases [16]
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