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pro vyhledávání: '"ROBERTS, Kirk"'
With the advancement of large language models (LLMs), the biomedical domain has seen significant progress and improvement in multiple tasks such as biomedical question answering, lay language summarization of the biomedical literature, clinical note
Externí odkaz:
http://arxiv.org/abs/2411.18069
Autor:
Hsu, Enshuo, Roberts, Kirk
Objectives: Despite the recent adoption of large language models (LLMs) for biomedical information extraction, challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Pyth
Externí odkaz:
http://arxiv.org/abs/2411.11779
Autor:
Hu, Yan, Zuo, Xu, Zhou, Yujia, Peng, Xueqing, Huang, Jimin, Keloth, Vipina K., Zhang, Vincent J., Weng, Ruey-Ling, Chen, Qingyu, Jiang, Xiaoqian, Roberts, Kirk E., Xu, Hua
Backgrounds: Information extraction (IE) is critical in clinical natural language processing (NLP). While large language models (LLMs) excel on generative tasks, their performance on extractive tasks remains debated. Methods: We investigated Named En
Externí odkaz:
http://arxiv.org/abs/2411.10020
Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantl
Externí odkaz:
http://arxiv.org/abs/2411.03805
Autor:
Yang, Yumeng, Krusche, Peter, Pantoja, Kristyn, Shi, Cheng, Ludmir, Ethan, Roberts, Kirk, Zhu, Gen
Tables, figures, and listings (TFLs) are essential tools for summarizing clinical trial data. Creation of TFLs for reporting activities is often a time-consuming task encountered routinely during the execution of clinical trials. This study explored
Externí odkaz:
http://arxiv.org/abs/2409.12046
Autor:
Hsu, Enshuo, Roberts, Kirk
The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily available or affordable. Weak supervision and in-context learning offer partia
Externí odkaz:
http://arxiv.org/abs/2406.06723
Autor:
Singh, Tavleen, Roberts, Kirk, Cohen, Trevor, Cobb, Nathan, Wang, Jing, Fujimoto, Kayo, Myneni, Sahiti
Publikováno v:
JMIR Public Health and Surveillance, Vol 6, Iss 4, p e21660 (2020)
BackgroundModifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media pl
Externí odkaz:
https://doaj.org/article/a544bfc0c67047e18f339a82a8e19305
Clinical trials are pivotal in medical research, and NLP can enhance their success, with application in recruitment. This study aims to evaluate the generalizability of eligibility classification across a broad spectrum of clinical trials. Starting w
Externí odkaz:
http://arxiv.org/abs/2403.17135
Question Answering (QA) systems on patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Significant amou
Externí odkaz:
http://arxiv.org/abs/2310.08759
Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language. A potential solution to this problem is to employ text classification methods for common type
Externí odkaz:
http://arxiv.org/abs/2309.07812