一个机器辅助的框架本体发展和标准化:数字健康技术的案例研究
Fang Chen1, Taylor B Harrison1,2, Sunyang Fu1
1McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
数字健康技术 (DHT) 提供个性化护理,但却产生了大量的文献. 使用大型语言模型的自适应本体框架为组织和增强DHT知识提供了一种半自动解决方案.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 知识管理知识管理
背景情况:
- 数字健康技术 (DHT) 正在改变医疗保健提供和研究.
- DHT文献的快速增长为知识组织和检索带来了重大挑战.
- 数字健康领域现有的本体学开发方法往往是手动的,阻碍了效率和可扩展性.
研究的目的:
- 提出和说明一个新的框架来管理不断扩大的数字健康知识.
- 解决数字健康领域传统手动本体论开发的局限性.
- 引入"自适应本体学"的概念,以系统和半自动地增强DHT本体学.
主要方法:
- 开发一个整合DHT词汇提取,本体学丰富和人类循环验证的框架.
- 使用大型语言模型 (LLM) 来支持自适应本体学概念.
- 应用框架系统地分类和增强数字健康本体学.
主要成果:
- 展示了一种半自动方法来分类和增强DHT本体学.
- 成功实施由LLMs.powered的自适应本体学.
- 一种用于管理不断变化的数字健康信息复杂性的实用方法.
结论:
- 拟议的自适应本体学框架为组织数字健康知识提供了一个高效和可扩展的解决方案.
- 基于LLM的自适应本体学可以系统地增强DHT信息的管理.
- 这种方法为导航数字健康研究和应用的动态格局提供了可行的途径.
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