5 citations · 5 across the 3 of their papers we have counts for
6 papers
Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset
Sumon Kanti Dey, Jeanne M. Powell, Azra Ismail +2
Nonmedical opioid use is an urgent public health challenge, with far-reaching clinical and social consequences that are often underreported in traditional healthcare settings. Soci…
Automated Thematic Analyses Using LLMs: Xylazine Wound Management Social Media Chatter Use Case
JaMor Hairston, Ritvik Ranjan, Sahithi Lakamana +4
Background Large language models (LLMs) face challenges in inductive thematic analysis, a task requiring deep interpretive and domain-specific expertise. We evaluated the feasibili…
Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR
Drew Walker, Jennifer Love, Swati Rajwal +4
Introduction: Electronic health records (EHR) are a critical medium through which patient stigmatization is perpetuated among healthcare teams. Methods: We identified linguistic fe…
Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods
Drew Walker, Swati Rajwal, Sudeshna Das +2
Social isolation and loneliness, which have been increasing in recent years strongly contribute toward suicide rates. Although social isolation and loneliness are not currently rec…
Large Language Models in Healthcare
Mohammed Al-Garadi, Tushar Mungle, Abdulaziz Ahmed +3
Large language models (LLMs) hold promise for transforming healthcare, from streamlining administrative and clinical workflows to enriching patient engagement and advancing clinica…
HILGEN: Hierarchically-Informed Data Generation for Biomedical NER Using Knowledgebases and Large Language Models
Yao Ge, Yuting Guo, Sudeshna Das +3
We present HILGEN, a Hierarchically-Informed Data Generation approach that combines domain knowledge from the Unified Medical Language System (UMLS) with synthetic data generated b…