EAGER: SCH: AI in Sleep for Rural and Aging Communities

EAGER：SCH：农村和老龄化社区睡眠中的人工智能

• 批准号: 2341551
• 负责人: Felicia Jefferson
• 金额: $ 29.93万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341551&HistoricalAwards=false
• 关键词: EAGER; SCH; AI; Sleep; Rural

Although artificial intelligence (AI) and machine learning have become commonplace, vulnerable populations often do not have input to the design, development, and application of key AI tools that improve the well-being and safety of communities. Reduced variety in the data input of AI tools diminishes the overall power of the technologies and the resulting output. This project uses AI technology to connect patients with sleep research professionals and specialists to obtain sleep health information in a longitudinal fashion. This approach provides support to populations who do not have regular access to sleep specialists, particularly rural and hard to reach populations such as shift workers and aging adults. The project also provides unique patient data to the AI tools to increase the patient population groups involved in optimizing this technology, which will mitigate input bias of the AI tools. This EAGER research effort is a unique opportunity for AI-based technologies for healthcare by having additional variation in population input, and by improving healthcare access to rural and vulnerable populations with a need for sustained access to sleep specialists. Individuals from these groups will be recruited, contributing to the technology’s overall input data producing broadly applicable output results.This project uses a newly synthesized and verified AI technology that uses the frequencies of physiological characteristics during sleep stages as inputs and analyzes resulting outputs for accuracy. The goals of the project are to: 1) optimize the data processing and algorithmic analysis of AI technologies by broadening the input data collected, improving the sensitivity, precision, and applicability of the output from deep learning algorithms for the public, 2) determine the accuracy of the AI technology’s data processing and interpretation and adaptability from unique population groups by comparing collected information with that of participants with similar backgrounds (for example, age, sex, and race) as the rural and other participant groups, and 3) increase access to needed population services through successful adaptability of specialized AI technologies. The project will explore the effectiveness using artificial neural networks in deep learning algorithms to interpret the raw data collected from participants as they sleep. Output produced from the AI tool and scoring of sleep studies (polysomnography) will be comparatively analyzed. The PIs will collaborate with sleep specialists, some who are located more than four hours from the participants’ home or work locations, for baseline polysomnography scoring and raw data analysis. Outputs will be compared with cohort data from similar groups. The outcomes of this project include providing more broad-based input for an AI analysis output function that is more predictable and accurate in analysis. Further, improvement in the health and well-being of the study group and the general population will result from better access to specialist care and use of precision tools for analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

尽管人工智能（AI）和机器学习已经变得司空见惯，但弱势群体往往无法参与设计、开发和应用改善社区福祉和安全的关键AI工具。人工智能工具数据输入的多样性减少，会削弱技术的整体能力和由此产生的输出。该项目使用人工智能技术将患者与睡眠研究专业人员和专家联系起来，以纵向方式获取睡眠健康信息。这种方法为无法定期获得睡眠专家的人群提供支持，特别是农村和难以接触的人群，如轮班工人和老年人。该项目还为人工智能工具提供了独特的患者数据，以增加参与优化该技术的患者人群，这将减轻人工智能工具的输入偏差。这项EAGER研究工作为基于人工智能的医疗保健技术提供了一个独特的机会，因为它增加了人口投入的变化，并改善了农村和弱势群体的医疗保健服务，这些人群需要持续获得睡眠专家的帮助。从这些群体中招募个人，为该技术的整体输入数据做出贡献，产生广泛适用的输出结果。该项目使用新合成和验证的AI技术，将睡眠阶段的生理特征频率作为输入，并分析结果输出的准确性。该项目的目标是：1）优化人工智能技术的数据处理和算法分析，扩大收集的输入数据，提高深度学习算法输出的灵敏度、精度和适用性，（二）通过将收集的信息与特定人群的信息进行比较，确定人工智能技术数据处理和解释的准确性以及对特定人群的适应性。与农村和其他参与者群体具有相似背景（例如，年龄，性别和种族）的参与者，以及3）通过专业人工智能技术的成功适应性增加所需人口服务的可及性。该项目将探索在深度学习算法中使用人工神经网络来解释参与者睡眠时收集的原始数据的有效性。将比较分析AI工具产生的输出和睡眠研究（多导睡眠图）的评分。PI将与睡眠专家合作，其中一些专家距离参与者的家庭或工作地点超过四小时，以进行基线多导睡眠图评分和原始数据分析。将输出与相似组的队列数据进行比较。该项目的成果包括为AI分析输出功能提供更广泛的输入，从而在分析中更具可预测性和准确性。此外，研究组和普通人群的健康和福祉的改善将来自于更好地获得专家护理和使用精确的分析工具。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。