SenSE: Multimodal Biosensors and Data driven Methods for Explainable Analytics for a Proactive approach to Heart Failure Care

SenSE：用于可解释分析的多模式生物传感器和数据驱动方法，用于主动治疗心力衰竭

• 批准号: 2037398
• 负责人: Emre Ertin
• 金额: $ 75万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037398&HistoricalAwards=false
• 关键词: SenSE; Multimodal; Biosensors; Data; driven

Congestive Heart Failure affects nearly six million Americans, with 670,000 diagnosed annually. Heart failure is one of the leading causes of hospital admission and readmission and death in the United States and one of the costliest disease syndromes. A major portion of this high cost of care is related to managing episodes of heart failure decompensation in the hospital. These recurring hospitalizations reduces the quality of life of heart failure patients, preventing them to lead productive and fulfilling lives. Ever rising costs, growing population of aging adults with chronic conditions necessitate new predictive, personalized and proactive approaches to cardiovascular health. The standard care to heart failure management relies on readily observable symptoms such as weight gain and labored breathing. Unfortunately, because these symptoms appear late in the course of heart failure decompensation, intervention is applied after hospitalization. In this proposal we pursue a proactive approach to care supported by innovations in noninvasive multimodal sensor systems paired with machine learning models for assessing the risk of heart failure decompensation and supporting interventions to prevent hospitalization in heart failure patients. The aims of this project are (a) Design, fabrication and validation an easy to use sensor patch that combines four key modalities to assess cardiac and lung function: Electrocardiogram (ECG). Bio Radio Frequency(RF), Bio-Impedance, and Seismocardiogram( SCG) (b) Learning of latent variable models for linking sensor measures to the risk of developing decompensated heart failure events with contextual information from electronic health records (EHR), and (c) Development of interpretable Deep Learning models for combining EHR data with multimodal sensor data for risk prediction and guiding therapy. The design of the sensor patch will explore new techniques integrating signals from a wide range of frequency bands into a single flexible board operating autonomously under a power budget. The joint sensor models developed in this project for ECG, SCG, Bio- RF and Impedance will provide insights into the noninvasive measures related to cardiovascular health previously only available to invasive methods such as implanted sensors and catheterizations. These non-invasive measures of cardiac health will be used to develop a learning based data fusion model for inferring latent health status quantified as decompensation risk. The project will result in interpretable deep learning models for combining multimodal EHR data with multimodal sensor data for early detection of compensated state and guiding medical interventions. These models will account for the sparse and non-uniform sampling of patient data in time, and employ learning of multi-modal embeddings for interpretability.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.

充血性心力衰竭影响着近600万美国人，每年诊断出67万人。心力衰竭是美国入院、再入院和死亡的主要原因之一，也是最昂贵的疾病综合征之一。这种高昂的护理成本的大部分与医院心力衰竭失代偿发作的管理有关。这些反复住院降低了心力衰竭患者的生活质量，阻止他们过上富有成效和充实的生活。不断上升的成本，不断增长的慢性病老年人需要新的预测，个性化和积极主动的心血管健康方法。心力衰竭管理的标准护理依赖于容易观察到的症状，如体重增加和呼吸困难。不幸的是，由于这些症状在心力衰竭失代偿过程中出现较晚，因此在住院后进行干预。在这项提案中，我们寻求一种积极主动的护理方法，由非侵入性多模态传感器系统的创新与机器学习模型相结合，用于评估心力衰竭失代偿的风险，并支持干预措施，以防止心力衰竭患者住院治疗。该项目的目标是：（a）设计、制造和验证一种易于使用的传感器贴片，该贴片结合了四种关键模式来评估心脏和肺功能：心电图（ECG）。生物射频（RF）、生物阻抗和地震心动图（SCG）（B）学习潜在变量模型，用于将传感器测量与来自电子健康记录（EHR）的背景信息的失代偿性心力衰竭事件发生风险联系起来，以及（c）开发可解释的深度学习模型，用于将EHR数据与多模态传感器数据相结合，以进行风险预测和指导治疗。传感器贴片的设计将探索新技术，将来自各种频带的信号集成到一个在功率预算下自主运行的单个柔性板中。本项目开发的ECG、SCG、生物射频和阻抗联合传感器模型将深入了解与心血管健康相关的非侵入性测量，这些测量以前仅适用于植入式传感器和导管插入术等侵入性方法。这些心脏健康的非侵入性测量将用于开发基于学习的数据融合模型，用于推断量化为失代偿风险的潜在健康状态。该项目将产生可解释的深度学习模型，用于将多模式EHR数据与多模式传感器数据相结合，以早期检测补偿状态并指导医疗干预。这些模型将解释患者数据的稀疏和非均匀采样，并采用多模态嵌入的学习来实现可解释性。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Bayesian Sparse Blind Deconvolution Using MCMC Methods Based on Normal-Inverse-Gamma Prior

使用基于正态逆伽玛先验的 MCMC 方法进行贝叶斯稀疏盲反卷积

• DOI: 10.1109/tsp.2022.3155877
• 发表时间: 2022
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Civek, Burak C.;Ertin, Emre
• 通讯作者: Ertin, Emre

Temporal Clustering with External Memory Network for Disease Progression Modeling

• DOI: 10.1109/icdm51629.2021.00107
• 发表时间: 2021-09
• 期刊: 2021 IEEE International Conference on Data Mining (ICDM)
• 作者: Zicong Zhang;Changchang Yin;Ping Zhang

Predicting Age-Related Macular Degeneration Progression with Contrastive Attention and Time-Aware LSTM.

• DOI: 10.1145/3534678.3539163
• 发表时间: 2022-08
• 期刊: KDD : proceedings. International Conference on Knowledge Discovery & Data Mining

Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning

通过多视图多任务学习对癌症幸存者进行心脏并发症风险分析

• 发表时间: 2021
• 期刊: Proceedings ICDM workshops
• 作者: Thai-Hoang Pham, Changchang Yin

DREAM: Domain Invariant and Contrastive Representation for Sleep Dynamics

• DOI: 10.1109/icdm54844.2022.00126
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Seungyeon Lee;Thai-Hoang Pham;Ping Zhang