Fetching the paper…
Reading the bibliography…
The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions.
Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies
Jason Lazarou, Bruce H Pomeranz, and Paul N Corey. 1998 · 1998
Earlier work this paper cites.
Incidence and preventability of adverse drug events in nursing homes
Jerry H Gurwitz, Terry S Field, Jerry Avorn, Danny McCormick, Shailavi Jain, Marie Eckler, Marcia Benser, Amy C Edmondson, and David W Bates. 2000 · 2000
Earlier work this paper cites.
Classifying semantic relations in bioscience texts
Barbara Rosario and Marti A Hearst. 2004 · 2004
Earlier work this paper cites.
Pharmacovigilance: ensuring the safe use of medicines
World Health Organization. 2004 · 2004
Earlier work this paper cites.
English annotation guidelines for events
Linguistic Data Consortium L D C LDC . 2005 · 2005
Earlier work this paper cites.
Building a semantically annotated corpus of clinical texts
Angus Roberts, Robert Gaizauskas, Mark Hepple, George Demetriou, Yikun Guo, Ian Roberts, and Andrea Setzer. 2009 · 2009
Earlier work this paper cites.
2010 i2b2/va challenge on concepts, assertions, and relations in clinical text
Özlem Uzuner, Brett R South, Shuying Shen, and Scott L DuVall. 2011 · 2010
Earlier work this paper cites.
Using a shallow linguistic kernel for drug–drug interaction extraction
Isabel Segura-Bedmar, Paloma Martinez, and Cesar de Pablo-Sánchez. 2011 · 2011
Earlier work this paper cites.
Using natural language processing to extract drug-drug interaction information from package inserts
Richard D Boyce, Gregory Gardner, and Henk Harkema. 2012 · 2012
Earlier work this paper cites.
Summary of product characteristics content extraction for a safe drugs usage
Stefania Rubrichi and Silvana Quaglini. 2012 · 2012
Earlier work this paper cites.
Brat: a web-based tool for nlp-assisted text annotation
Pontus Stenetorp, Sampo Pyysalo, Goran Topić, Tomoko Ohta, Sophia Ananiadou, and Jun’ichi Tsujii. 2012 · 2012
Earlier work this paper cites.
The eu-adr corpus: annotated drugs, diseases, targets, and their relationships
Erik M Van Mulligen, Annie Fourrier-Reglat, David Gurwitz, Mariam Molokhia, Ainhoa Nieto, Gianluca Trifiro, Jan A Kors, and Laura I Furlong. 2012 · 2012
Earlier work this paper cites.
The ddi corpus: An annotated corpus with pharmacological substances and drug–drug interactions
María Herrero-Zazo, Isabel Segura-Bedmar, Paloma Martínez, and Thierry Declerck. 2013 · 2013
Earlier work this paper cites.
Mining twitter for adverse drug reaction mentions: a corpus and classification benchmark
Rachel Ginn, Pranoti Pimpalkhute, Azadeh Nikfarjam, Apurv Patki, Karen O’Connor, Abeed Sarker, Karen Smith, and Graciela Gonzalez. 2014 · 2014
Cited alongside, same era.
Mining adverse drug reaction signals from social media: going beyond extraction
Apurv Patki, Abeed Sarker, Pranoti Pimpalkhute, Azadeh Nikfarjam, Rachel Ginn, Karen O’Connor, Karen Smith, and Graciela Gonzalez. 2014 · 2014
Cited alongside, same era.
Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features
Azadeh Nikfarjam, Abeed Sarker, Karen O’connor, Rachel Ginn, and Graciela Gonzalez. 2015 · 2015
Cited alongside, same era.
Adverse drug reaction classification with deep neural networks
Trung-Tin Huynh, Yulan He, Alistair Willis, and Stefan M. Rüger. 2016 · 2016
Cited alongside, same era.
Biomedical event extraction using convolutional neural networks and dependency parsing
Jari Björne and Tapio Salakoski. 2018 · 2018
Cited alongside, same era.
Biomedical event extraction with hierarchical knowledge graphs
Kung-Hsiang Huang, Mu Yang, and Nanyun Peng. 2020 · 2020
Later among the works it cites.
An ensemble of neural models for nested adverse drug events and medication extraction with subwords
Meizhi Ju, Nhung TH Nguyen, Makoto Miwa, and Sophia Ananiadou. 2020 · 2020
Later among the works it cites.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020 · 2020
Later among the works it cites.
Event extraction as multi-turn question answering
Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, and Yong Zhu. 2020b · 2020
Later among the works it cites.
A joint neural model for information extraction with global features
Ying Lin, Heng Ji, Fei Huang, and Lingfei Wu. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records
Sam Henry, Kevin Buchan, Michele Filannino, Amber Stubbs, and Ozlem Uzuner. 2020 · 2018
Cited alongside, same era.
Extracting biomedical events with parallel multi-pooling convolutional neural networks
Lishuang Li, Yang Liu, and Meiyue Qin. 2018 · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
Cited alongside, same era.
Annotation and detection of drug effects in text for pharmacovigilance
Paul Thompson, Sophia Daikou, Kenju Ueno, Riza Batista-Navarro, Jun’ichi Tsujii, and Sophia Ananiadou. 2018 · 2018
Cited alongside, same era.
Overview of the first natural language processing challenge for extracting medication, indication, and adverse drug events from electronic health record notes (made 1.0)
Abhyuday Jagannatha, Feifan Liu, Weisong Liu, and Hong Yu. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Biomedical event extraction based on knowledge-driven tree-lstm
Diya Li, Lifu Huang, Heng Ji, and Jiawei Han. 2020a · 2019
Cited alongside, same era.
Jian Liu, Yubo Chen, Kang Liu, Wei Bi, and Xiaojiang Liu. 2020 · 2020
Later among the works it cites.
Biomedical event extraction as sequence labeling
Alan Ramponi, Rob van der Goot, Rosario Lombardo, and Barbara Plank. 2020 · 2020
Later among the works it cites.
Deepeventmine: end-to-end neural nested event extraction from biomedical texts
Hai-Long Trieu, Thy Thy Tran, Khoa NA Duong, Anh Nguyen, Makoto Miwa, and Sophia Ananiadou. 2020 · 2020
Later among the works it cites.
A study of deep learning approaches for medication and adverse drug event extraction from clinical text
Qiang Wei, Zongcheng Ji, Zhiheng Li, Jingcheng Du, Jingqi Wang, Jun Xu, Yang Xiang, Firat Tiryaki, Stephen Wu, Yaoyun Zhang, et al. 2020 · 2020
Later among the works it cites.
Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks
Lvxing Zhu and Haoran Zheng. 2020 · 2020
Later among the works it cites.
Scifive: a text-to-text transformer model for biomedical literature
Long N Phan, James T Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, and Grégoire Altan-Bonnet. 2021 · 2021
Later among the works it cites.
Automated concatenation of embeddings for structured prediction
Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, and Kewei Tu. 2021 · 2021
Later among the works it cites.