MESH: Multimodal Estimators for Sensing Health

MESH：用于感知健康状况的多模态估计器

• 批准号: 10714073
• 负责人: Rose Faghih
• 金额: $ 36.52万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714073
• 关键词: Affect; Algorithms; Awareness; Biological Response Modifiers; Cardiac Surgery procedures; Clinical; Clinical Data; Data; Data Set; Devices; Diagnosis; Disease; Endocrinology; Eye Movements; Fatigue; Feedback; Goals; Health; Homeostasis; Hormones; Immune response; Infection; Inflammation; Interdisciplinary Study; Intervention; Knowledge; Label; Laboratories; Measurement; Medical; Metabolism; Modeling; Monitor; Neurons; Neurosciences; Operative Surgical Procedures; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Physicians; Physiologic Monitoring; Physiological; Physiological Processes; Play; Production; Psychiatry; Public Health; Publishing; Recovery; Research; Rheumatology; Role; Signal Transduction; Stimulus; Stress; System; cytokine; data de-identification; health care quality; improved; individualized medicine; insight; mathematical algorithm; monitoring device; multimodality; neurosurgery; portability; programs; response; wearable monitor

Project Summary/Abstract The PI’s goal is to develop an interdisciplinary research program and a foundational algorithmic framework for reliably inferring health states from physiological signals acquired using wearable and portable physiological monitoring devices. Uncovering the health states will unleash an array of applications related to monitoring inflammation, metabolism, fatigue and interoceptive awareness. For instance, it is well known that hormones play an important role in maintaining homeostasis of the body, while cytokines are crucial as mediators of immune response after surgery or infection that disturbs this homeostasis. Adverse external influences such as stress can profoundly alter the hormone or cytokine production in patients, affecting their health and complicating recovery from diseases or surgery. The knowledge of their secretion and modulation in response to major influences such as cardiac surgery, medications, disease, and stress is crucial to the health of patients, more so when more than one of these factors is concurrently present. Thus, there is a compelling but unfulfilled need to quantify hidden health states of inflammation, metabolism, fatigue and interoceptive awareness. The PI’s laboratory seeks to pioneer system-theoretic computational toolsets for understanding the pulsatile signaling underlying the physiological signals (e.g., cytokines, hormones, eye movement) related to different health states and capturing the unobserved temporal dynamics of one’s health states in a biologically plausible manner while considering extensive experimental settings and clinical data. This project will determine the pulsatile physiological signaling from discrete, noisy measurements by performing signal deconvolution to extract the neuronal stimuli underlying their modulation, and will build decoders to quantify internal health states that are indicative of inflammation, metabolism, fatigue and interoceptive awareness using both unlabeled information as well as labels via feedback from patients and clinicians, to help physicians interpret physiological data and inform patient-specific treatment in a holistic manner. The proposed research will use de-identified data both from publicly available datasets and those collected by the PI’s collaborators (e.g., endocrinology, rheumatology, neurosurgery, psychiatry, neuroscience) using wearable or portable devices to perform signal analysis and compare the results against previously published results, known experimental settings, and clinical knowledge to validate the models and provide new insight.

项目概要/摘要 PI 的目标是开发跨学科研究项目和基础算法 根据使用可穿戴设备获取的生理信号可靠地推断健康状态的框架 和便携式生理监测设备将释放一系列健康状态。 与监测炎症、代谢、疲劳和内感受相关的应用程序 例如，众所周知，激素在维持意识方面发挥着重要作用。 身体的稳态，而细胞因子作为免疫反应的介质至关重要 扰乱这种稳态的手术或感染，例如压力。 可以广泛改变患者体内激素或细胞因子的产生，影响他们的健康和 使疾病或手术的恢复变得复杂。它们的分泌和调节的知识。 应对心脏手术、药物、疾病和压力等重大影响 对患者的健康至关重要，当其中一个以上因素同时存在时更是如此 因此，迫切需要量化隐藏的健康状态，但尚未实现。 PI 实验室致力于研究炎症、新陈代谢、疲劳和内感受意识。 用于理解脉动信号传导的先驱系统理论计算工具集 与以下相关的生理信号（例如细胞因子、激素、眼球运动）的基础 不同的健康状态并捕捉一个人的健康状态的未观察到的时间动态 在考虑广泛的实验设置和临床的同时，采用生物学上合理的方式 该项目将从离散的、嘈杂的数据中确定脉动生理信号。 通过执行信号反卷积来提取其背后的神经刺激来进行测量 调制，并将构建解码器来量化指示的内部健康状态 使用未标记信息的炎症、新陈代谢、疲劳和内感受意识 以及通过患者和圣人的反馈提供的标签，以帮助医生解释 生理数据并以整体方式为患者的具体治疗提供信息。 研究将使用来自公开数据集和以下机构收集的去识别化数据 PI 的合作者（例如内分泌学、风湿病学、神经外科、精神病学、 神经科学）使用可穿戴或便携式设备进行信号分析并比较 结果与先前发表的结果、已知的实验设置和临床知识相比较 验证模型并提供新的见解。