Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports

作者：Gurulingappa Harsha^*; Rajput Abdul Mateen; Roberts Angus; Fluck Juliane; Hofmann Apitius Martin; Toldo Luca

来源：Journal of Biomedical Informatics, 2012, 45(5): 885-892.

DOI：10.1016/j.jbi.2012.04.008

• Adverse drug effect
• Benchmark corpus
• Annotation
• Harmonization
• Sentence classification

A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F-1 score of 0.70 indicating a potential useful application of the corpus.

• 出版日期2012-10