Výsledky vyhledávání - "Keloth, Vipina"

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Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?

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

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Me LLaMA: Foundation Large Language Models for Medical Applications

Autor: Xie, Qianqian, Chen, Qingyu, Chen, Aokun, Peng, Cheng, Hu, Yan, Lin, Fongci, Peng, Xueqing, Huang, Jimin, Zhang, Jeffrey, Keloth, Vipina, Zhou, Xinyu, Qian, Lingfei, He, Huan, Shung, Dennis, Ohno-Machado, Lucila, Wu, Yonghui, Xu, Hua, Bian, Jiang

Recent advancements in large language models (LLMs) like ChatGPT and LLaMA show promise in medical applications, yet challenges remain in medical language comprehension. This study presents Me-LLaMA, a new medical LLM family based on open-source LLaM

Externí odkaz: http://arxiv.org/abs/2402.12749

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A systematic evaluation of large language models for biomedical natural language processing: benchmarks, baselines, and recommendations

The biomedical literature is rapidly expanding, posing a significant challenge for manual curation and knowledge discovery. Biomedical Natural Language Processing (BioNLP) has emerged as a powerful solution, enabling the automated extraction of infor

Externí odkaz: http://arxiv.org/abs/2305.16326

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Integrating Commercial and Social Determinants of Health: A Unified Ontology for Non-Clinical Determinants of Health

Autor: Kollapally, Navya Martin, Keloth, Vipina Kuttichi, Xu, Julia, Geller, James

The objectives of this research are 1) to develop an ontology for CDoH by utilizing PubMed articles and ChatGPT; 2) to foster ontology reuse by integrating CDoH with an existing SDoH ontology into a unified structure; 3) to devise an overarching conc

Externí odkaz: http://arxiv.org/abs/2304.01446

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Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering

Autor: Hu, Yan, Chen, Qingyu, Du, Jingcheng, Peng, Xueqing, Keloth, Vipina Kuttichi, Zuo, Xu, Zhou, Yujia, Li, Zehan, Jiang, Xiaoqian, Lu, Zhiyong, Roberts, Kirk, Xu, Hua

Objective: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. Materials and Methods: We evaluated these models on two clinical

Externí odkaz: http://arxiv.org/abs/2303.16416

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Systematic Design and Evaluation of Social Determinants of Health Ontology (SDoHO)

Autor: Dang, Yifang, Li, Fang, Hu, Xinyue, Keloth, Vipina K., Zhang, Meng, Fu, Sunyang, Du, Jingcheng, Fan, J. Wilfred, Amith, Muhammad F., Yu, Evan, Liu, Hongfang, Jiang, Xiaoqian, Xu, Hua, Tao, Cui

Social determinants of health (SDoH) have a significant impact on health outcomes and well-being. Addressing SDoH is the key to reducing healthcare inequalities and transforming a "sick care" system into a "health promoting" system. To address the SD

Externí odkaz: http://arxiv.org/abs/2212.01941

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