Chapter title |
Extracting Adverse Drug Events from Text Using Human Advice
|
---|---|
Chapter number | 26 |
Book title |
Artificial Intelligence in Medicine
|
Published in |
Artificial intelligence in medicine : 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015 : proceedings. Conference on Artificial Intelligence in Medicine (2005-) (15th : 2015 : Pavia, Italy), January 2015
|
DOI | 10.1007/978-3-319-19551-3_26 |
Pubmed ID | |
Book ISBNs |
978-3-31-919550-6, 978-3-31-919551-3
|
Authors |
Phillip Odom, Vishal Bangera, Tushar Khot, David Page, Sriraam Natarajan |
Editors |
John H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek |
Abstract |
Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society in general. When methods extract ADEs from observational data, there is a necessity to evaluate these methods. More precisely, it is important to know what is already known in the literature. Consequently, we employ a novel relation extraction technique based on a recently developed probabilistic logic learning algorithm that exploits human advice. We demonstrate on a standard adverse drug events data base that the proposed approach can successfully extract existing adverse drug events from limited amount of training data and compares favorably with state-of-the-art probabilistic logic learning methods. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | 9% |
Unknown | 10 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 3 | 27% |
Student > Master | 3 | 27% |
Student > Ph. D. Student | 2 | 18% |
Student > Doctoral Student | 1 | 9% |
Professor | 1 | 9% |
Other | 0 | 0% |
Unknown | 1 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 5 | 45% |
Biochemistry, Genetics and Molecular Biology | 2 | 18% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 9% |
Philosophy | 1 | 9% |
Engineering | 1 | 9% |
Other | 0 | 0% |
Unknown | 1 | 9% |