Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Text Mining for Biology And Biomedicine
Text Mining for Biology And Biomedicine
Sequence Labelling SVMs Trained in One Pass
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Using a shallow linguistic kernel for drug-drug interaction extraction
Journal of Biomedical Informatics
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
A system for the extraction and representation of summary of product characteristics content
Artificial Intelligence in Medicine
The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions
Journal of Biomedical Informatics
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The use of medications has a central role in health care provision, yet on occasion, it may injure the person taking them as result of adverse drug events. A correct drug choice must be modulated to acknowledge both patients' status and drug-specific information. However, this information is locked in free-text and, as such, cannot be actively accessed and elaborated by computerized applications. The goal of this work lies in extracting content (active ingredient, interaction effects, etc.) from the Summary of Product Characteristics, focusing mainly on drug-related interactions, following a machine learning based approach. We compare two state of the art classifiers: conditional random fields with support vector machines. To this end, we introduce a corpus of 100 interaction sections, hand annotated with 13 labels that have been derived from a previously developed conceptual model. The results of our empirical analysis demonstrate that the two models perform well. They exhibit similar overall performance, with an overall accuracy of about 91%.