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人医疗数据。这些数据可以包括各种生理信息，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是如何变化的，并对未来的结果做出预测。人工智能（AI）模型在从医疗数据中发现见解和了解疾病进展方面非常有帮助。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），这些注释可能成本高昂且难以获得。例如，虽然重症监护病房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数据，以记录重要事件。此外，这些注释可能不可靠，因为医生可能对病人或事件有不同的看法。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的深刻理解。总体而言，该项目旨在推动智慧医疗的发展，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来学习医学时间序列数据的表示。具体而言，该项目将侧重于以下任务：(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)应用所建立的框架分析脑电图（EEG）信号，用于阿尔茨海默病（AD）的诊断；(3)通过构建医学图，将框架扩展到多模态医学时间序列数据，该医学图对不同医疗实体之间的依赖关系进行建模，并通过图消息传递集成表示；(4)将所得模型应用于使用多模态生命信号预测临床结果，重点是通过学习图注意权重提高可解释性。研究者将向医学界传播自我监督方法的好处，并组织专题和研讨会，以促进医疗保健弱监督方法的研究。因此，该项目将进一步为利用先进的人工智能模型扩大医疗系统奠定基础，并通过加快决策过程减轻医生的负担。对于人工智能和医疗保健等跨学科领域的教育，该项目将为学生提供开创性的知识，同时为年轻科学家提供现实世界的案例研究和实用材料。该奖项反映了美国国家科学基金会的法定使命，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。

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.