Výsledky vyhledávání - "Flores, Mona G"

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Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need

Autor: Peng, Cheng, Yang, Xi, Chen, Aokun, Yu, Zehao, Smith, Kaleb E, Costa, Anthony B, Flores, Mona G, Bian, Jiang, Wu, Yonghui

Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning. Methods We formulated 7 key clinical NLP tasks as text-to

Externí odkaz: http://arxiv.org/abs/2312.06099

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A Study of Generative Large Language Model for Medical Research and Healthcare

Autor: Peng, Cheng, Yang, Xi, Chen, Aokun, Smith, Kaleb E, PourNejatian, Nima, Costa, Anthony B, Martin, Cheryl, Flores, Mona G, Zhang, Ying, Magoc, Tanja, Lipori, Gloria, Mitchell, Duane A, Ospina, Naykky S, Ahmed, Mustafa M, Hogan, William R, Shenkman, Elizabeth A, Guo, Yi, Bian, Jiang, Wu, Yonghui

There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using 277 billi

Externí odkaz: http://arxiv.org/abs/2305.13523

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Do Gradient Inversion Attacks Make Federated Learning Unsafe?

Autor: Hatamizadeh, Ali, Yin, Hongxu, Molchanov, Pavlo, Myronenko, Andriy, Li, Wenqi, Dogra, Prerna, Feng, Andrew, Flores, Mona G., Kautz, Jan, Xu, Daguang, Roth, Holger R.

Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare applications where patient and data privacy is of utmost concern. However, recent

Externí odkaz: http://arxiv.org/abs/2202.06924

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GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records

Autor: Yang, Xi, Chen, Aokun, PourNejatian, Nima, Shin, Hoo Chang, Smith, Kaleb E, Parisien, Christopher, Compas, Colin, Martin, Cheryl, Flores, Mona G, Zhang, Ying, Magoc, Tanja, Harle, Christopher A, Lipori, Gloria, Mitchell, Duane A, Hogan, William R, Shenkman, Elizabeth A, Bian, Jiang, Wu, Yonghui

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI

Externí odkaz: http://arxiv.org/abs/2203.03540

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Deep Learning-Based Automatic Detection of Poorly Positioned Mammograms to Minimize Patient Return Visits for Repeat Imaging: A Real-World Application

Autor: Gupta, Vikash, Taylor, Clayton, Bonnet, Sarah, Prevedello, Luciano M., Hawley, Jeffrey, White, Richard D, Flores, Mona G, Erdal, Barbaros Selnur

Screening mammograms are a routine imaging exam performed to detect breast cancer in its early stages to reduce morbidity and mortality attributed to this disease. In order to maximize the efficacy of breast cancer screening programs, proper mammogra

Externí odkaz: http://arxiv.org/abs/2009.13580

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Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning

Autor: Gupta1, Vikash, Roth, Holger, Buch3, Varun, Rockenbach, Marcio A. B. C., White, Richard D, Yang, Dong, Laur, Olga, Ghoshhajra, Brian, Dayan, Ittai, Xu, Daguang, Flores, Mona G., Erdal, Barbaros Selnur

The training of deep learning models typically requires extensive data, which are not readily available as large well-curated medical-image datasets for development of artificial intelligence (AI) models applied in Radiology. Recognizing the potentia

Externí odkaz: http://arxiv.org/abs/2009.12437

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Akademický článek

Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need.

Autor: Peng, Cheng¹ (AUTHOR), Yang, Xi^1,2 (AUTHOR), Chen, Aokun^1,2 (AUTHOR), Yu, Zehao¹ (AUTHOR), Smith, Kaleb E³ (AUTHOR), Costa, Anthony B³ (AUTHOR), Flores, Mona G³ (AUTHOR), Bian, Jiang^1,2 (AUTHOR), Wu, Yonghui^1,2 (AUTHOR) yonghui.wu@ufl.edu

Publikováno v: Journal of the American Medical Informatics Association. Sep2024, Vol. 31 Issue 9, p1892-1903. 12p.