Title |
Knowledge-based extraction of adverse drug events from biomedical text
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Published in |
BMC Bioinformatics, March 2014
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DOI | 10.1186/1471-2105-15-64 |
Pubmed ID | |
Authors |
Ning Kang, Bharat Singh, Chinh Bui, Zubair Afzal, Erik M van Mulligen, Jan A Kors |
Abstract |
Many biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The system consists of a concept recognition module that identifies drugs and adverse effects in sentences, and a knowledge-base module that establishes whether a relation exists between the recognized concepts. The knowledge base was filled with information from the Unified Medical Language System. The performance of the system was evaluated on the ADE corpus, consisting of 1644 abstracts with manually annotated adverse drug events. Fifty abstracts were used for training, the remaining abstracts were used for testing. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 20% |
Spain | 1 | 20% |
Norway | 1 | 20% |
United Kingdom | 1 | 20% |
Unknown | 1 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 80% |
Members of the public | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 3 | 2% |
France | 2 | 2% |
United States | 2 | 2% |
Australia | 1 | <1% |
Portugal | 1 | <1% |
Japan | 1 | <1% |
Spain | 1 | <1% |
Unknown | 110 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 32 | 26% |
Researcher | 22 | 18% |
Student > Master | 13 | 11% |
Student > Doctoral Student | 9 | 7% |
Student > Bachelor | 7 | 6% |
Other | 21 | 17% |
Unknown | 17 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 61 | 50% |
Agricultural and Biological Sciences | 10 | 8% |
Medicine and Dentistry | 8 | 7% |
Engineering | 5 | 4% |
Social Sciences | 3 | 2% |
Other | 13 | 11% |
Unknown | 21 | 17% |