MESH: Multimodal Estimators for Sensing Health

项目摘要/摘要 PI的目标是开发一个跨学科的研究计划和一个基础算法 从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。揭示运行状况将释放一系列 与监测炎症、新陈代谢、疲劳和内感相关的应用 意识。例如，众所周知，荷尔蒙在维持身体健康方面起着重要作用。 体内的动态平衡，而细胞因子是免疫反应的关键介质，在 动手术或感染会扰乱这种内环境平衡。不利的外部影响，如压力 可以深刻改变患者的激素或细胞因子的产生，影响他们的健康和 使疾病或手术后的恢复复杂化。对它们的分泌和调节的知识 对心脏手术、药物、疾病和压力等重大影响的反应是 对患者的健康至关重要，当这些因素中的一个以上同时存在时更是如此 现在时。因此，有一种迫切但尚未满足的需求，即量化隐藏的健康状态 炎症、新陈代谢、疲劳和内感意识。私家侦探的实验室试图 用于理解脉动信号的先锋系统理论计算工具集 潜在的生理信号(例如，细胞因子、激素、眼球运动)与 不同的健康状态和捕获一个人的健康状态的未被观测的时间动态 在考虑广泛的实验环境和临床情况的同时，以生物学上可信的方式 数据。本项目将从离散的、噪声的、脉动的生理信号中确定 通过执行信号去卷积来提取其背后的神经元刺激来进行测量 调制，并将构建解码器来量化内部健康状态，以指示 使用这两种未标记信息的炎症、新陈代谢、疲劳和内感意识 以及通过患者和临床医生的反馈获得标签，以帮助医生解释 生理数据，并以整体方式告知针对患者的治疗。建议数 研究将使用来自公开可用的数据集和由收集的数据的去识别数据 PI的合作者(例如，内分泌学、风湿学、神经外科、精神病学、 神经科学)使用可穿戴或便携式设备执行信号分析并比较 结果与以前发表的结果、已知的实验环境和临床知识相对照 以验证模型并提供新的见解。

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.