1 citations · 2 across the 4 of their papers we have counts for
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Harnessing Large Language Models for Biomedical Named Entity Recognition
Jian Chen, Leilei Su, Cong Sun
Background and Objective: Biomedical Named Entity Recognition (BioNER) is a foundational task in medical informatics, crucial for downstream applications like drug discovery and cl…
Extracting Post-Acute Sequelae of SARS-CoV-2 Infection Symptoms from Clinical Notes via Hybrid Natural Language Processing
Zilong Bai, Zihan Xu, Cong Sun +13
Accurately and efficiently diagnosing Post-Acute Sequelae of COVID-19 (PASC) remains challenging due to its myriad symptoms that evolve over long- and variable-time intervals. To a…
Generative Large Language Models Trained for Detecting Errors in Radiology Reports
Cong Sun, Kurt Teichman, Yiliang Zhou +8
In this retrospective study, a dataset was constructed with two parts. The first part included 1,656 synthetic chest radiology reports generated by GPT-4 using specified prompts, w…
A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review
Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor +12
With generative artificial intelligence (AI), particularly large language models (LLMs), continuing to make inroads in healthcare, it is critical to supplement traditional automate…
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension
Leilei Su, Jian Chen, Yifan Peng +1
Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot…
Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge
Cong Sun, Zhihao Yang, Leilei Su +4
Motivation: The biomedical literature contains a wealth of chemical-protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for d…