Identifying Medication Abuse and Adverse Effects from Tweets: University of Michigan at #SMM4H 2020
V.G.Vinod Vydiswaran | Deahan Yu | Xinyan Zhao | Ermioni Carr | Jonathan Martindale | Jingcheng Xiao | Noha Ghannam | Matteo Althoen | Alexis Castellanos | Neel Patel | Daniel Vasquez
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
The team from the University of Michigan participated in three tasks in the Social Media Mining for Health Applications (#SMM4H) 2020 shared tasks – on detecting mentions of adverse effects (Task 2), extracting and normalizing them (Task 3), and detecting mentions of medication abuse (Task 4). Our approaches relied on a combination of traditional machine learning and deep learning models. On Tasks 2 and 4, our submitted runs performed at or above the task average.
Assessing the Feasibility of an Automated Suggestion System for Communicating Critical Findings from Chest Radiology Reports to Referring Physicians
Brian E. Chapman | Danielle L. Mowery | Evan Narasimhan | Neel Patel | Wendy Chapman | Marta Heilbrun
Proceedings of the 15th Workshop on Biomedical Natural Language Processing
- V.G.Vinod Vydiswaran 1
- Deahan Yu 1
- Xinyan Zhao 1
- Ermioni Carr 1
- Jonathan Martindale 1
- show all...