Prediction of Heart Failure Onset using Multimodal Data Analysis, Deep Learning and Commercial Wearables

使用多模式数据分析预测心力衰竭的发病 学习和商用可穿戴设备 项目摘要/摘要 研究：心力衰竭是美国死亡率的主要原因之一，也是医疗费用的驱动因素 各州。到2030年，心力衰竭患者的数量预计将达到800万。如果我们能预测谁会 发生心力衰竭，这将创造一个机会，通过以下方式改善患者的体验和预后 启动更早的行为和治疗干预。电子健康记录(EHR)包含信息 这可以用来在心力衰竭发作之前预测它。然而，现有的模型导致了大量的 假阳性预测的可能性，限制了它们的临床应用。PI建议通过以下方式增加电子病历数据 心电图和心率变异性(HRV)特征，以提高预测准确性 提前12个月出现心力衰竭。三种数据模式(电子病历、心电和心率变异性)将是 使用深度学习方法进行分析，包括PI提出的新技术。模特们将会是 使用密歇根医学公司提供的患者数据进行了回顾性开发和验证。第二个目标是 建议通过用以下方法取代临床测量的心电图和心率变异性来增加这项研究的影响 由智能手表等可穿戴消费设备获得的产品。一组预期的患者将穿着 可穿戴设备七天，这将允许PI确定是否收集到信息 (间歇心电、来自光体积描记的连续HRV和活动描记)，与EHR相结合， 可以为临床医生提供一种更有效的工具来识别哪些患者有心力衰竭的风险。虽然这件事 这种方法将使更多的患者受益，但仍将仅限于有既往病史的患者。至 进一步将这项研究的影响扩大到那些佩戴消费可穿戴设备但以前没有佩戴的人 病史，仅依赖于可穿戴设备收集的信息的有限模型将是 已评估。因此，这项研究的结果将包括针对不同人群的多种模型，例如 有或没有既往病史的人。应聘者/职业发展：萨达尔·安萨里博士是一名计算机 在生物医学信号处理、机器学习和医学方面有专长的科学家和统计学家 可穿戴设备。他过去的研究经验包括分析心电信号以改进检测 减少重症监护病房中的心律失常和错误警报；检测和消除噪音和运动 生物医学信号中的伪影，如心电和生物阻抗；血流动力学失代偿的预测 使用HRV；检测失血性休克、透析中低血压和低心脏指数 可穿戴技术。这一奖项将使安萨里博士获得所需的心血管方面的额外培训 生理学和心力衰竭病理生理学通过导师、授课培训、参加研讨会和 科学会议和临床暴露，为他专注于开发的独立职业生涯做好准备 心血管医学的诊断和临床决策支持工具。

Prediction of Heart Failure Onset using Multimodal Data Analysis, Deep Learning and Commercial Wearables Project Summary/Abstract Research: Heart failure is one of the leading causes of mortality and drivers of healthcare costs in the United States. By 2030, the number of heart failure patients is projected to reach 8 million. If we could predict who will develop heart failure, this would create an opportunity to improve patient experiences and outcomes by initiating earlier behavioral and therapeutic interventions. Electronic health records (EHR) contain information that can be used to predict heart failure before its onset. However, the existing models lead to a large number of false positive predictions, limiting their clinical utility. The PI proposes to augment the EHR data with electrocardiogram (ECG) and heart rate variability (HRV) features to improve the accuracy of predicting the onset of heart failure 12 months in advance. The three modalities of data (EHR, ECG and HRV) will be analyzed using deep learning methods, including novel techniques proposed by the PI. The models will be developed and validated retrospectively using patient data available at Michigan Medicine. The second aim of the proposal is to increase the impact of this research by replacing the clinically measured ECG and HRV with those obtained by consumer wearables such as smart watches. A prospective cohort of patients will wear a wearable device for seven days, which will allow the PI to determine whether the collected information (intermittent ECG, continuous HRV derived from photoplethysmography, and actigraphy), combined with EHR, can provide clinicians with a more effective tool to identify which patients are at risk of heart failure. While this approach will benefit a larger population of patients, it will still be limited to those with past medical history. To further expand the impact of this research to those who wear consumer wearables but have no previous medical history, a limited model that depends only on the information gathered by the wearable device will be evaluated. Thus, the outcomes of this study will include multiple models targeting various populations, such as those with and without prior medical history. Candidate / Career Development: Dr. Sardar Ansari is a computer scientist and statistician with expertise in biomedical signal processing, machine learning, and medical wearable devices. His past research experience includes analysis of ECG signal to improve detection of cardiac arrhythmias and reduce false alarms in intensive care units; detection and removal of noise and motion artifacts in biomedical signals such as ECG and bioimpedance; prediction of hemodynamic decompensation using HRV; and detection of hemorrhagic shock, intradialytic hypotension, and low cardiac index using wearable technology. This award will allow Dr. Ansari to acquire needed additional training in cardiovascular physiology and heart failure pathophysiology through mentorship, didactic training, attending workshops and scientific meetings, and clinical exposure, preparing him for an independent career focused on developing diagnostic and clinical decision support tools for cardiovascular medicine.