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Found 8 papers, 4 papers with code
Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model
We present and release PHS-BERT, a transformer-based PLM, to identify tasks related to public health surveillance on social media.
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations
The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which requires finding all relevant trials.
Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT
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.
Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU
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.
Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States
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).
Recommending research articles to consumers of online vaccination information
Online health communications often provide biased interpretations of evidence and have unreliable links to the source research.
Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations
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.
Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials.gov
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
