CRII:SCH:Self-Supervised Contrastive Representation Learning for Medical Time Series

CRII：SCH：医学时间序列的自监督对比表示学习

• 批准号: 2245894
• 负责人: Xiang Zhang
• 金额: $ 17.5万
• 依托单位: University of North Carolina at Charlotte
• 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: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245894&HistoricalAwards=false
• 关键词: CRII; SCH; Self; Supervised; Contrastive

Medical time series data includes an individual's medical data that are collected over a period of time. The data can include a variety of physiological information, such as brain activity, heart rate, and/or blood pressure. By analyzing medical time series data, researchers and healthcare providers can gain a better understanding of how a patient's health is changing and make predictions about future outcomes. Artificial intelligence (AI) models can be very helpful in uncovering insights from medical data and understanding the progression of a disease. However, using AI techniques can require a large number of high-quality professional annotations (notes by healthcare providers), which can be costly and hard to obtain. For example, while devices in intensive care units can continuously monitor vital signs, physicians may only have the time to review and annotate a small portion of the data to note important events. Moreover, the annotations may not be reliable because doctors may have different opinions patients or events. To this end, this project will build innovative technologies to provide insightful understanding of a patient’s health with minimal expert input. Overall, this project aims to promote the development of smart healthcare, relieve the burden on physicians, and enhance the quality of life.This project will develop a novel self-supervised contrastive framework to learn representations from medical time series data. Specifically, the project will focus on the following tasks: (1) developing a frequency-aware contrastive framework for unimodal time series data, which leverages the cohesion between time-based and frequency-based representations of the same sample; (2) applying the established framework to analyze Electroencephalography (EEG) signals for the diagnosis of Alzheimer's Disease (AD); (3) extending the framework to multimodal medical time series data by constructing a medical graph that models the dependencies among diverse medical entities and integrates representations through graph message passing; and (4) applying the resulting model to predict clinical outcomes using multimodal vital signals, with a focus on improving interpretability through the learned graph attention weights. The investigator will disseminate the benefits of self-supervised methods to the medical community, and organize special issues and workshops to promote research in weakly-supervised methods for healthcare. This project, thereby, will further lay the groundwork for augmenting the medical system with advanced AI models, and reduce the burden on physicians by accelerating the decision-making process. For education in the interdisciplinary area of AI and healthcare, this project will deliver pioneering knowledge to students while providing real-world case studies and practical materials to young scientists.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.

医疗时间序列数据包括在一段时间内收集的个人医疗数据，研究人员通过分析医疗时间序列数据可以包括各种生理信息，例如大脑活动、心率和/或血压。医疗保健提供者可以更好地了解患者的健康状况如何变化，并对未来的结果进行预测。然而，使用人工智能可以非常有助于从医疗数据中发现见解并了解疾病的进展。技术可能需要大量高质量的专业注释（医疗保健提供者的注释）可能成本高昂且难以获得，例如，虽然重症监护病房中的设备可以持续监测生命体征，但医生可能只有时间检查和注释一小部分。此外，由于医生可能对患者或事件有不同的意见，因此注释可能不可靠。为此，该项目将构建创新技术，以最少的专家投入提供对患者健康状况的深入了解。该项目旨在促进发展智能医疗，减轻医生负担，提高生活质量。该项目将开发一种新颖的自监督对比框架来学习医疗时间序列数据的表示，具体而言，该项目将重点关注以下任务：（1）开发用于单峰时间序列数据的频率感知对比框架，该框架利用同一样本的基于时间和基于频率的表示之间的凝聚力；（2）应用已建立的框架来分析脑电图（EEG）信号以诊断阿尔茨海默病；疾病(AD)；(3) 通过构建医学图来将框架扩展到多模式医疗时间序列数据，该医学图对不同医疗实体之间的依赖关系进行建模并通过图消息传递来集成表示；(4) 应用所得模型来预测临床结果；多模态生命信号，重点是通过学习的图形注意力权重来提高可解释性。研究人员将向医学界传播自我监督方法的好处，并组织特别问题和研讨会，以促进弱监督医疗方法的研究。这个项目， ，将进一步为利用先进的人工智能模型增强医疗系统奠定基础，从而通过加速决策过程来减轻医生的负担，对于人工智能和医疗保健跨学科领域的教育，该项目将提供开创性的知识。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。