146 citations · 258 across the 14 of their papers we have counts for
5 papers · 1 filter
Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
Yikuan Li, Hanyin Wang, Halid Yerebakan +2
In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. We leveraged the LLM to convert clinical texts into their…
A Comparative Study of Pretrained Language Models for Long Clinical Text
Yikuan Li, Ramsey M. Wehbe, Faraz S. Ahmad +2
Objective: Clinical knowledge enriched transformer models (e.g., ClinicalBERT) have state-of-the-art results on clinical NLP (natural language processing) tasks. One of the core li…
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
Chengsheng Mao, Jie Xu, Luke Rasmussen +9
Objective: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from…
Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets
Hanyin Wang, Meghan R. Hutch, Yikuan Li +6
Over 12 billion doses of COVID-19 vaccines have been administered at the time of writing. However, public perceptions of vaccines have been complex. We analyzed COVID-19 vaccine-re…
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
Yikuan Li, Ramsey M. Wehbe, Faraz S. Ahmad +2
Transformers-based models, such as BERT, have dramatically improved the performance for various natural language processing tasks. The clinical knowledge enriched model, namely Cli…