Extraction of Adverse Drug Effects from Medical Case Reports
نویسندگان
چکیده
A sheer amount of information about adverse effects of drugs are published in medical case reports that pose major challenges for drug safety experts to perform timely monitoring. Efficient strategies for identification and extraction of information about adverse drug effects from free-text resources are needed to support pharmacovigilance research and decision making. Therefore, this work focusses on the adaptation of a machine learning-based relation extraction system for the identification and extraction of drug-related adverse effects from MEDLINE case reports. It relies on a high quality corpus that was manually annotated, using ontology-driven methodology. Qualitative evaluation of the system show robust results.
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Pattern-based System to Extract and Distinguish Drug- Adverse Effect Relation from Other Drug-medical Condition Relations in the Same Sentence
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