通过大型语言模型改变阿尔茨海默氏症的数字护理
Sujin Kim1, Dong Y Han2, Jihye Bae3
1Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine University of Kentucky, Lexington, 230F, Multidisciplinary Science Building, 725 Rose Street, KY40536, USA.
Current Alzheimer research
|November 26, 2024
概括
本研究探讨使用人工智能大型语言模型 (LLM) 来创建阿尔茨海默病和相关痴呆症 (AD/ADRD) 的数字照顾策略. 研究结果表明,LLM可以为护理人员提供个性化的支持,从而改善患者的护理.
科学领域:
- 老年学是一门学科.
- 人工智能的人工智能
- 数字健康数字健康
背景情况:
- 阿尔茨海默病和相关痴呆症 (AD/ADRD) 对非正式照顾者造成了重大负担.
- 现有的护理教育材料 (CEM) 和移动应用程序描述 (MAD) 为数字干预提供了基础.
- 人们越来越认识到对AD/ADRD照顾者的适应性和不断发展的支持系统的需要.
研究的目的:
- 调查人工智能驱动的大型语言模型 (LLM) 在开发AD/ADRD数字护理策略方面的潜力.
- 分析现有的CEM和MAD,以确定关键的护理任务和数字功能.
- 将护理任务与AD进展的不同阶段的数字解决方案结合起来.
主要方法:
- 来自MedlinePlus的38个CEM和57个AD数字护理MAD的分析.
- 利用ChatGPT 3.5提取关键的护理任务,并将它们与每个AD阶段的适当数字功能相匹配.
- 确定了对护理人员的数字素养要求,这些护理人员支持患有AD/ADRD的人.
主要成果:
- 艾滋病护理被分为四个阶段 (临床前,轻度,中度,重度) 和相关的关键任务.
- 识别的数字辅助包括增强记忆的应用程序,GPS跟踪和语音控制设备.
- 确定了六种基本的数字素养技能:基本的数字技能,沟通,信息管理,安全/隐私,医疗保健知识和护理人员协调.
结论:
- 倡导一个LLM驱动的策略来设计AD/ADRD的数字护理干预措施.
- 提出了AD/ADRD支持的新范式,提供根据护理人员需求量身定制的适应性援助.
- 旨在通过人工智能驱动的数字工具增强护理人员的共享决策和患者护理能力.
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