no code implementations • nlppower (ACL) 2022 • Usman Naseem, Byoung Chan Lee, Matloob Khushi, Jinman Kim, Adam G. Dunn
We present and release PHS-BERT, a transformer-based PLM, to identify tasks related to public health surveillance on social media.
1 code implementation • Findings (EMNLP) 2021 • Shifeng Liu, Yifang Sun, Bing Li, Wei Wang, Florence T. Bourgeois, Adam G. Dunn
The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which requires finding all relevant trials.
1 code implementation • 9 Jul 2021 • Usman Naseem, Adam G. Dunn, Matloob Khushi, Jinman Kim
We present BioALBERT, a domain-specific adaptation of A Lite Bidirectional Encoder Representations from Transformers (ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical (MIMIC-III) corpora and fine tuned for 6 different tasks across 20 benchmark datasets.
no code implementations • 17 Jun 2021 • Usman Naseem, Matloob Khushi, Jinman Kim, Adam G. Dunn
In this study, to classify vaccine sentiment tweets with limited information, we present a novel end-to-end framework consisting of interconnected components that use domain-specific LM trained on vaccine-related tweets and models commonsense knowledge into a bidirectional gated recurrent network (CK-BiGRU) with context-aware attention.
no code implementations • 6 Jul 2019 • Hansi Zhang, Christopher Wheldon, Adam G. Dunn, Cui Tao, Jinhai Huo, Rui Zhang, Mattia Prosperi, Yi Guo, Jiang Bian
We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).
1 code implementation • 26 Apr 2019 • Eliza Harrison, Paige Martin, Didi Surian, Adam G. Dunn
Online health communications often provide biased interpretations of evidence and have unreliable links to the source research.
no code implementations • 22 Feb 2018 • Zubair Shah, Paige Martin, Enrico Coiera, Kenneth D. Mandl, Adam G. Dunn
Background: Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications.
1 code implementation • 7 Sep 2017 • Adam G. Dunn, Enrico Coiera, Florence Bourgeois
Performance was measured by the median rank of matching articles, and the proportion of unreported links that could be found by screening ranked candidate articles in order.
Information Retrieval H.2.8; H.3.3; J.3