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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.

项目摘要（摘要）