ADRTrace: Detecting Expected and Unexpected Adverse Drug Reactions from User Reviews on Social Media Sites
By Andrew Yates, Nazli Goharian. 2013
104 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jimmy J. Lin
- Sean MacAvaney
- Nazli Goharian
- Rodrigo Nogueira
- Andrew Yates
- Arman Cohan
- Zeynep Akkalyoncu Yilmaz
- Sebastian Hofstätter
In Computational Theory and Mathematics
- Abeed Sarker
- Rodrigo Nogueira
- Jimmy J. Lin
- Sean MacAvaney
- Arman Cohan
- Andrew Yates
- Nazli Goharian
- Graciela Gonzalez
In Molecular Biology
- Jimmy J. Lin
- Rodrigo Nogueira
- Arman Cohan
- Sean MacAvaney
- Abeed Sarker
- Andrew Yates
- Nazli Goharian
- Graciela Gonzalez
In Natural Language Processing
- Jimmy J. Lin
- Rodrigo Nogueira
- Sean MacAvaney
- Nazli Goharian
- Andrew Yates
- Arman Cohan
- Abeed Sarker
- Zeynep Akkalyoncu Yilmaz
Citations in the corpus
- Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “llection and retaining only queries (and their associated relevant passages) that contain at least one term in the MedSyn lexicon, which includes layperson terminology for various medical conditions [Yates and Goharian, 2013]. That is, MacAvaney et al. [2020a] used this simple technique to create a ”
- Portable Automatic Text Classification for Adverse Drug Reaction Detection via Multi-corpus Training (2014, background): “However, since they have to rely on the development of lexicons, these works are generally limited in the number of drugs studied or the number of target ADRs ( e.g. , [26] focuses on 4 drugs, [27] on 10 drugs and 5 ADRs, [28] on breast cancer associated ADRs only, and [29] on 5 drugs).”
- SLEDGE: A Simple Yet Effective Baseline for Coronavirus Scientific Knowledge Search (2020, methodology): “To produce this subset, we use the MedSyn lexicon (Yates and Goharian, 2013), which includes layperson terminology for various medical conditions.”
- Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features (2015, background): “…for medical concepts), 34 Medical Dictionary for Regulatory Activities (MedDRA), 35 and Coding Symbols for a Thesaurus of Adverse Reaction Terms (COSTART), most prior studies 11,12,14,36–38 focused on exploring existing or customized/expanded lexicons to find ADR mentions in user posts.”
- Utilizing Social Media Data for Pharmacovigilance: A Review (2015, methodology): “With the creation of recent publicly available corpora ( e.g., [50,38,35,58], learning algorithms can be trained and optimized to detect ADR assertive instances with high accuracies.”