1 code implementation • 20 Feb 2024 • Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, Xinyu Zhou, Huan He, Lucila Ohno-Machado, Yonghui Wu, Hua Xu, Jiang Bian
In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets.
no code implementations • 3 Feb 2024 • Aokun Chen, Qian Li, Yu Huang, Yongqiu Li, Yu-Neng Chuang, Xia Hu, Serena Guo, Yonghui Wu, Yi Guo, Jiang Bian
We constructed an interactive knowledge map to disseminate our study results.
no code implementations • 11 Dec 2023 • Cheng Peng, Xi Yang, Aokun Chen, Zehao Yu, Kaleb E Smith, Anthony B Costa, Mona G Flores, Jiang Bian, Yonghui Wu
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.
no code implementations • 11 Oct 2023 • Amin Dada, Aokun Chen, Cheng Peng, Kaleb E Smith, Ahmad Idrissi-Yaghir, Constantin Marc Seibold, Jianning Li, Lars Heiliger, Xi Yang, Christoph M. Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, Yonghui Wu
Traditionally, large language models have been either trained on general web crawls or domain-specific data.
no code implementations • 10 Oct 2023 • Cheng Peng, Xi Yang, Kaleb E Smith, Zehao Yu, Aokun Chen, Jiang Bian, Yonghui Wu
We evaluated the transfer learning ability of the prompt-based learning algorithms in a cross-institution setting.
1 code implementation • 22 May 2023 • Cheng Peng, Xi Yang, Aokun Chen, Kaleb E Smith, Nima PourNejatian, Anthony B Costa, Cheryl Martin, Mona G Flores, Ying Zhang, Tanja Magoc, Gloria Lipori, Duane A Mitchell, Naykky S Ospina, Mustafa M Ahmed, William R Hogan, Elizabeth A Shenkman, Yi Guo, Jiang Bian, Yonghui Wu
This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare.
no code implementations • 31 Mar 2023 • Aokun Chen, Daniel Paredes, Zehao Yu, Xiwei Lou, Roberta Brunson, Jamie N. Thomas, Kimberly A. Martinez, Robert J. Lucero, Tanja Magoc, Laurence M. Solberg, Urszula A. Snigurska, Sarah E. Ser, Mattia Prosperi, Jiang Bian, Ragnhildur I. Bjarnadottir, Yonghui Wu
To assist in the diagnosis and phenotyping of delirium, we formed an expert panel to categorize diverse delirium symptoms, composed annotation guidelines, created a delirium corpus with diverse delirium symptoms, and developed NLP methods to extract delirium symptoms from clinical notes.
no code implementations • 14 Mar 2023 • Aokun Chen, Zehao Yu, Xi Yang, Yi Guo, Jiang Bian, Yonghui Wu
Materials and methods: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes.
no code implementations • 2 Feb 2022 • Xi Yang, Aokun Chen, Nima PourNejatian, Hoo Chang Shin, Kaleb E Smith, Christopher Parisien, Colin Compas, Cheryl Martin, Mona G Flores, Ying Zhang, Tanja Magoc, Christopher A Harle, Gloria Lipori, Duane A Mitchell, William R Hogan, Elizabeth A Shenkman, Jiang Bian, Yonghui Wu
GatorTron models scale up the clinical language model from 110 million to 8. 9 billion parameters and improve 5 clinical NLP tasks (e. g., 9. 6% and 9. 5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery.
Ranked #10 on Zero-Shot Learning on MedConceptsQA
no code implementations • 20 May 2021 • Luiz Giovanini, Fabrício Ceschin, Mirela Silva, Aokun Chen, Ramchandra Kulkarni, Sanjay Banda, Madison Lysaght, Heng Qiao, Nikolaos Sapountzis, Ruimin Sun, Brandon Matthews, Dapeng Oliver Wu, André Grégio, Daniela Oliveira
This paper investigates whether computer usage profiles comprised of process-, network-, mouse-, and keystroke-related events are unique and consistent over time in a naturalistic setting, discussing challenges and opportunities of using such profiles in applications of continuous authentication.
no code implementations • 4 Dec 2017 • Ruimin Sun, Xiaoyong Yuan, Pan He, Qile Zhu, Aokun Chen, Andre Gregio, Daniela Oliveira, Xiaolin Li
Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead.