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

医疗时间序列数据包括在一段时间内收集的个体的医疗数据。数据可以包括各种生理信息，诸如大脑活动、心率和/或血压。通过分析医疗时间序列数据，研究人员和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。人工智能（AI）模型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。然而，使用人工智能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），这可能是昂贵的，难以获得。例如，虽然重症监护室中的设备可以连续监测生命体征，但医生可能只有时间查看和注释一小部分数据以记录重要事件。此外，注释可能不可靠，因为医生可能对患者或事件有不同的意见。为此，该项目将建立创新技术，以最少的专家投入提供对患者健康的深入了解。总的来说，本项目旨在促进智能医疗的发展，减轻医生的负担，提高生活质量。本项目将开发一种新的自监督对比框架，从医疗时间序列数据中学习表示。具体而言，该项目将集中于以下任务：（1）开发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内聚性;（2）将建立的框架应用于分析脑电（EEG）信号以诊断阿尔茨海默病（AD）;（3）通过构建医学图来将框架扩展到多模态医学时间序列数据，所述医学图对不同医学实体之间的依赖性进行建模并且通过图消息传递来集成表示;以及（4）应用所得到的模型以使用多模态生命信号来预测临床结果，重点在于通过学习的图形注意力权重来提高可解释性。研究者将向医学界宣传自我监督方法的好处，并组织特别问题和研讨会，以促进对弱监督方法的研究。因此，该项目将进一步为使用先进的人工智能模型增强医疗系统奠定基础，并通过加速决策过程来减轻医生的负担。在人工智能和医疗保健的跨学科教育领域，该项目将向学生提供开创性的知识，同时向年轻科学家提供真实世界的案例研究和实用材料。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。

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.