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

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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.