2026· International Conference on Language Resources and Evaluation· pp. 10802-10814· 0 citations· 28 references
Computer Science
TL;DR
It is demonstrated that integrating compact neural encoders with large language models provides a scalable and highly accurate framework for clinical NLP tasks, including SDoH extraction, and some unexpected patterns in LLM performance are observed.
This approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships and successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support s...
U. Luke, P. Asuquo, Victor Anaga et al.· E3S Web of Conferences· 0 citations
The findings demonstrate that a leakage-safe evaluation protocol is essential for producing credible results in mental health classification from long social media posts and show that domain-informed density extraction provides a robust and practical text representation, with its greatest benefits emerging when models...
Rafat Hammad, Suha Rababah· Frontiers in Artificial Inte...· 0 citations
A comparative study of machine learning and transformer-based approaches for clinical domain classification from medical transcriptions and provides a systematic comparison of classical machine learning, transformer models, and few-shot prompting for medical transcription analysis.
Sravani Pottipati, Lakshmikar R. Polamreddy· 0 citations
OBJECTIVE
Clinical trials (CTs) are essential for advancing disease diagnosis and treatment; however, recruiting eligible participants remains a major bottleneck. Existing recruitment strategies, such as advertisements and electronic health record-based screening, are often time-consuming and inefficient. Social networ...
Xiaofan Zhou, Zi-Su Wang, Janice L. Krieger et al.· Journal of Biomedical Inform...· 0 citations
The results indicate that the proposed explainable framework could accurately classify the posts on Twitter as belonging to any or all of the major mental health categories, and the classification could be interpreted clinically.
Misha M, Hamza Muneer, R. Tehseen et al.· International Journal of Inn...· 0 citations
An integrated framework for artificial intelligence prediction of both individual and population burden of chronic diseases among adolescents from electronic health records, measures of social determinants and liver ultrasound images improved risk stratification and disease burden estimation over conventional and unimo...
M. Sridhar· Natural Resources for Human...· 0 citations
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