Penn Artificial Intelligence and Technology Collaboratory for Healthy Aging

宾夕法尼亚大学健康老龄化人工智能与技术合作实验室

基本信息

批准号：
10862939
负责人：
George Demiris
金额：
$ 32万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-30 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10862939
关键词：
Address Administrative Supplement Adult Aging Alzheimer&apos s Disease Alzheimer&apos s disease caregiver Alzheimer&apos s disease related dementia Anxiety Artificial Intelligence Behavior Therapy Caregivers Caring Clinical Clinical Trials Computer software Data Data Set Databases Dedications Dementia caregivers Development Evaluation Family Caregiver Future Genetic Transcription Goals Grant Health Healthcare Systems Home Home environment Human Industry Intervention Interview Language Managed Care Mental Depression Modeling Monitor Names Outcome Parents Patients Performance Pilot Projects Process Public Health Quality of life Research Services Shapes Technology Text Training Transcript U-Series Cooperative Agreements United States National Institutes of Health Work aging population artificial intelligence method care systems collaboratory commercialization coping data repository dementia care falls healthy aging improved innovation innovative technologies mental state new technology open source public health relevance response tool

项目摘要

Project Summary (Abstract) Successful aging in the home can greatly improve quality of life, improve health outcomes, and reduce burden on the healthcare system. The goal of the Parent P30 is to establish a national collaboratory, named PennAITech, for the development, evaluation, and implementation of artificial intelligence software and new technologies to facilitate health aging in the home. Recent advances in AI have led to the development of highly notable Large Language Models (LLMs) such as OpenAI’s ChatGPT. These models have showcased exceptional abilities in comprehending and producing text that resembles human language to a remarkable extent, leading to a great potential to reshape the AI assistance research in caring for aging population. In this supplement, we propose a pilot project to develop and explore powerful AI assisted tools using LLMs to support caring for the aging population. We focus our study on family caregivers of patients with Alzheimer’s Disease and Related Dementias (ADRD) and examine whether LLMs can be developed to answer questions that caregivers have. Since most LLMs are trained on text data from various domains, their ability for specific domains may not be optimized. Thus, the overarching goal of this supplement is to collect high-quality data in our domain, and use that to finetune the LLMs to make them more powerful to answer domain specific questions. Furthermore, this work will highlight future directions for research of LLM specifically in the context of ADRD care. To achieve this goal, we have two aims. In Aim 1, we will create a conversational data repository specific to behavior intervention for family caregivers of persons with dementia to improve their quality of life. We will generate, clean and preprocess the interview transcripts from behavior intervention sessions for family caregivers of persons with dementia from an ongoing qualitative data repository, which includes sessions among caregivers and therapists (N= 3,000 as of 6/1/23). In Aim 2, we will build a large language model (LLM) to provide an AI assisted, efficient and scalable approach in supporting behavior intervention for dementia caregivers. We propose to use the high-quality data from the conversational database generated in Aim 1 to finetune the existing powerful LLMs, and build an LLM suitable for answering questions from dementia caregivers to help reduce their anxiety and depression, improve their mental status and quality of life. The resulting LLM is expected to provide answers that closely align with those of human experts, offering an AI-assisted, efficient, and scalable approach to behavioral interventions for family caregivers of dementia patients. Also, this approach can be extended to develop LLMs for other relevant applications, such as addressing clinical questions related to ADRD. By doing so, this could provide valuable AI- enabled services to the aging care industry, contributing to the overall improvement of public health.

项目概要（摘要）成功的在家养老可以极大地提高生活质量、改善健康状况并减轻负担 Parent P30 的目标是建立一个名为 PennAITech 的国家合作机构，开发、评估和实施人工智能软件和新技术促进家庭健康老龄化。人工智能的最新进展极大地促进了大型技术的发展。语言模型 (LLM)，例如 OpenAI 的 ChatGPT，这些模型在这方面展示了卓越的能力。理解和生成在很大程度上类似于人类语言的文本，从而产生了巨大的影响重塑人工智能援助研究在照顾老龄化人口方面的潜力。在本增刊中，我们提出了一项建议。使用法学硕士开发和探索强大的人工智能辅助工具以支持老年人护理的试点项目我们的研究重点是阿尔茨海默病和相关痴呆症患者的家庭护理人员。（ADRD）并研究是否可以开发法学硕士来回答大多数护理人员所面临的问题。法学硕士接受来自各个领域的文本数据的培训，他们针对特定领域的能力可能不会得到优化。本补充的总体目标是收集我们领域的高质量数据，并利用这些数据来微调法学硕士使他们更有能力回答特定领域的问题此外，这项工作将突出。法学硕士的未来研究方向，特别是在 ADRD 护理方面，为了实现这一目标，我们有两个目标。在目标 1 中，我们将创建一个专门针对家庭行为干预的对话数据存储库。我们将生成、清洁和预处理痴呆症患者的护理人员，以改善他们的生活质量。为痴呆症患者的家庭照顾者提供的行为干预会议访谈记录持续的定性数据存储库，其中包括护理人员和治疗师之间的会议（截至目前 N= 3,000 6/1/23）在目标 2 中，我们将构建一个大型语言模型（LLM）以提供人工智能辅助、高效且可扩展的模型。我们建议使用高质量的数据来支持痴呆症护理人员的行为干预。从目标 1 中生成的会话数据库中对现有强大的 LLM 进行微调，并构建 LLM 适合回答痴呆症护理人员的问题，帮助减轻他们的焦虑和抑郁，改善他们的精神状态和生活质量预计将提供与他们的精神状态和生活质量密切相关的答案。人类专家的研究成果，提供人工智能辅助、高效且可扩展的行为干预方法此外，这种方法可以扩展到其他相关的法学硕士。应用程序，例如解决与 ADRD 相关的临床问题，这可以提供有价值的人工智能。服务养老产业，促进公共卫生整体改善。