Preventing adverse drug events by extracting information from drug fact sheets

Stefania Rubrichi, Alex Spengler, Patrick Gallinari, Silvana Quaglini
2010 International Symposium for Semantic Mining in Biomedicine  
The increasing volume and growing complexity of drugs lead to an increased risk of prescription errors and adverse events. A correct drug choice must be modulated to acknowledge both patients' status and drug-specific information. This information is reported in free-text on drug fact sheets. It is often overwhelming and difficult to access. There is thus a rising need for generating comprehensive and structured data that help prevent such events by improving access to fact sheet information.
more » ... is work presents a machine learning based system for the automatic prediction of drug-related entities (active ingredient, interaction effects, etc.) in textual drug fact sheets, focusing on drug interactions. Results: Our approach learns to classify this information in the structured prediction framework, comparing conditional random fields and support vector machines. Both classifiers are trained and evaluated using a corpus of 100 drug fact sheets. They have been hand-annotated with fourteen semantic labels that have been derived from a previously developed domain ontology. Our experimental results show that the two models exhibit similar overall performance. They achieve an average F 1 -measure of about 93 per cent, which is promising. The performance results of both models on the individual labels are also comparably good. Conclusions: We have shown that it is possible to perform the task of information extraction from drug fact sheets using supervised machine learning techniques. Although we have focused on drug interactions, the encouraging results and the adaptability of the approach we adopted means that our system has general significance for the extraction of detailed information on drugs (drug targets, contraindications, side effects, etc.).
dblp:conf/smbm/RubrichiSGQ10 fatcat:tn2whf3xunbspizqzkg7nmmlgu