CAREER: Improving Real-world Performance of AI Biosignal Algorithms

职业：提高人工智能生物信号算法的实际性能

• 批准号: 2339669
• 负责人: Jessilyn Dunn
• 金额: $ 59.78万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339669&HistoricalAwards=false
• 关键词: CAREER; Improving; Real; world; Performance

AI-based algorithms for processing individual biological data streams (biosignals) are the enabling technology underlying wearables like smartwatches and medical monitors that are pivotal for health monitoring in everyday life. Current algorithms, despite their utility in wearables for health monitoring, are hindered by performance disparities, particularly among diverse demographic groups. This project addresses critical challenges surrounding biased data and constantly changing technologies, or drift, leading to reduced accuracy, especially for marginalized groups. The research focuses on evaluating how well algorithms perform across diverse people, various types of measurements, over time, and with technology updates. Outputs of this project promise to enhance the fairness and reliability of AI technologies for monitoring biosignals, offering breakthrough methods for equitable and reliable health monitoring outside of the clinic. The research plan unfolds in two primary thrusts. The first thrust develops robust techniques for assessing and reporting algorithm performance across intersectional populations, with a particular focus on regression tasks involving continuous variables. This includes characterizing existing biosignal training dataset demographics, designing reporting standards, and implementing a theory-based method for quantitative evaluation of algorithmic fairness while considering intersectionality. An empirical analysis will assess intersectional fairness on key biosignal algorithms and datasets. The second thrust focuses on detecting and monitoring concept drift over time in biosignal data and algorithms, accounting for intersectional demographic shifts. This involves developing methods and metrics for concept drift monitoring, providing a nuanced understanding of how changes in training data composition impact the performance of AI-based biosignal algorithms. This work will result in gaining fundamental knowledge about bias and drift, advancing techniques for their detection and monitoring, and, ultimately, enhancing the equitable and reliable application of AI in biosignal algorithms for improved health outcomes. The project's scope also extends to an outreach and education plan, promoting increased access to biosignal monitoring devices and fostering diversity in STEM fields. This multifaceted approach ensures that the impact of the research transcends theoretical advancements, directly benefiting society through the improvement of wearables for health monitoring as well as the development of methods to enable more general AI oversight. Reflecting NSF’s statutory mission, this project will provide societal benefits through development of and education on trustworthy and equitable AI technologies and their applications to biosignal algorithms to improve health and wellness broadly.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.

用于处理个体生物数据流（生物信号）的基于AI的算法是智能可穿戴设备（如智能手表和医疗监视器）的支持技术，这些可穿戴设备在日常生活中是健康监测的关键。当前的算法是在可穿戴设备中进行健康监测的实用性，受到绩效差异的阻碍，尤其是在潜水员人群中。该项目解决了围绕有偏见的数据以及不断变化的技术或漂移的关键挑战，导致准确性降低，尤其是对于边缘化组而言。该研究重点是评估算法在潜水员，各种类型的测量，随着时间的流逝以及技术更新的效果。该项目的产出有望提高AI技术来监测生物信号的公平和可靠性，从而为诊所以外的公平和可靠的健康监测提供突破性方法。研究计划以两个主要的推力展开。第一个推力开发了可靠的技术，用于评估和报告跨间口的算法性能，特别关注回归任务涉及持续变量。这包括表征现有的生物信号培训数据集人口统计数据，设计报告标准，以及实施一种基于理论的方法来定量评估算法公平性，同时考虑交叉性。经验分析将评估关键生物信号算法和数据集的交叉公平性。第二个推力着重于在生物信号数据和算法中检测和监视概念随时间的漂移，从而考虑了交叉人口统计学的变化。这涉及开发用于概念漂移监测的方法和指标，从而对训练数据组成的变化如何影响基于AI的生物信号算法的性能有细微的理解。这项工作将导致获得有关偏见和漂移的基本知识，推进其检测和监测技术，并最终增强AI在生物信号算法中的公平和可靠应用以改善健康结果。该项目的范围还扩展到了推广和教育计划，从而促进了增加生物信号监测设备的访问权限，并促进了STEM领域的多样性。这种多方面的方法确保了研究的影响超越理论的进步，通过改善可穿戴设备进行健康监测以及开发方法，从而使社会受益，从而使社会受益。反映NSF的法定使命，该项目将通过开发和公平的AI技术的发展和教育来提供社会利益，及其在生物信号算法上的应用，以广泛改善健康和健康。这奖反映了NSF的法定任务，并通过该基金会的知识优点和广泛的效果来评估NSF的法定任务，并通过评估值得进行评估。