Times are displayed in (UTC-08:00) Pacific Time (US & Canada) Change
11/9/2024 |
1:00 PM – 4:30 PM |
Franciscan A
W21: Development and Evaluation of Large Language Models in Healthcare Applications
Presentation Type: Workshop/Tutorial
Development and Evaluation of Large Language Models in Healthcare Applications
Presentation Time: 01:00 PM - 04:30 PM
Abstract Keywords: Natural Language Processing, Large Language Models (LLMs), Informatics Implementation
Working Group: Natural Language Processing Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Language models are being increasingly used in natural language processing (NLP) applications, which require neither the development of a task-specific architecture nor customized training on large datasets. In particular, large language models (LLMs), such as the GPT, PaLM, and Llama-2, have demonstrated significant advances in NLP tasks. On the other hand, concerns have also been raised about the impact of these tools on health care. Although it is widely accepted that LLMs should be used with integrity, transparency, and honesty, how to appropriately do so and, if needed, regulate the development, and use of this technology needs further discussion.
This course provides students with an understanding of LLMs and their applications in health. Students will acquire knowledge of natural language processing, large language models, chain-of-though, Retrieval-Augmented Generation (RAG), and the range of prompting methods available for processing clinical text. Hands-on experience and a toolkit will provide useful skills for managing text data to solve a variety of problems in the health domain.
We believe that the proposed tutorial is timely and urgently needed for AMIA stakeholders, including informaticists from a broad array of disciplines, clinicians, software developers, and IT professionals, to learn how to develop and use these models to ensure that their potential benefits are realized while any potential risks and negative consequences are minimized. This tutorial will also likely be one of many conversations at AMIA 2024 about this issue as we learn more about LLMs, their capacity, and their potential impact on healthcare.
Speaker(s):
Yifan Peng, PhD
Weill Cornell Medicine; Dept of Population Health Sciences; Div of Health Informatics
Yanshan Wang, PhD
University of Pittsburgh
Hua Xu, Ph.D
Yale University
Author(s):
Presentation Time: 01:00 PM - 04:30 PM
Abstract Keywords: Natural Language Processing, Large Language Models (LLMs), Informatics Implementation
Working Group: Natural Language Processing Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Language models are being increasingly used in natural language processing (NLP) applications, which require neither the development of a task-specific architecture nor customized training on large datasets. In particular, large language models (LLMs), such as the GPT, PaLM, and Llama-2, have demonstrated significant advances in NLP tasks. On the other hand, concerns have also been raised about the impact of these tools on health care. Although it is widely accepted that LLMs should be used with integrity, transparency, and honesty, how to appropriately do so and, if needed, regulate the development, and use of this technology needs further discussion.
This course provides students with an understanding of LLMs and their applications in health. Students will acquire knowledge of natural language processing, large language models, chain-of-though, Retrieval-Augmented Generation (RAG), and the range of prompting methods available for processing clinical text. Hands-on experience and a toolkit will provide useful skills for managing text data to solve a variety of problems in the health domain.
We believe that the proposed tutorial is timely and urgently needed for AMIA stakeholders, including informaticists from a broad array of disciplines, clinicians, software developers, and IT professionals, to learn how to develop and use these models to ensure that their potential benefits are realized while any potential risks and negative consequences are minimized. This tutorial will also likely be one of many conversations at AMIA 2024 about this issue as we learn more about LLMs, their capacity, and their potential impact on healthcare.
Speaker(s):
Yifan Peng, PhD
Weill Cornell Medicine; Dept of Population Health Sciences; Div of Health Informatics
Yanshan Wang, PhD
University of Pittsburgh
Hua Xu, Ph.D
Yale University
Author(s):
W21: Development and Evaluation of Large Language Models in Healthcare Applications
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