Search Results for author:
Found 8 papers, 4 papers with code
Detecting Health Advice in Medical Research Literature
We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.
“Devils Are in the Details”: Annotating Specificity of Clinical Advice from Medical Literature
Prior studies have raised concerns over specificity issues in clinical advice.
From explainable to interpretable deep learning for natural language processing in healthcare: how far from reality?
This work presents a thorough scoping review on explainable and interpretable DL in healthcare NLP.
Measuring Pointwise $\mathcal{V}$-Usable Information In-Context-ly
In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models.
Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification
The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice.
Measuring Correlation-to-Causation Exaggeration in Press Releases
By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22{\%} of press releases made exaggerated causal claims from correlational findings in observational studies.
Detecting Causal Language Use in Science Findings
We then applied the prediction model to measure the causal language use in the research conclusions of about 38, 000 observational studies in PubMed.
An NLP Analysis of Exaggerated Claims in Science News
The discrepancy between science and media has been affecting the effectiveness of science communication.
