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Machine learning techniques for passive, remote monitoring of elderly heart failure patients from home

用于在家中被动远程监测老年心力衰竭患者的机器学习技术

基本信息

批准号：
10187779
负责人：
Brian Francis Bender
金额：
$ 22.5万
依托单位：
BENDER TECH, LLC
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-01 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10187779
关键词：
Acute Biometry Cardiology Characteristics Classification Clinical Trials Collection Consensus Data Data Collection Defecation Devices Diuretics Dose Economic Burden Effectiveness Elderly Engineering European Fingerprint Frequencies Future Goals Health Care Costs Health behavior Heart failure Home Hospitalization Individual Internet Machine Learning Manuals Maps Measurement Measures Medicare Modeling Outcome Output Patients Performance Phase Physiological Population Potassium Prevalence Prognostic Marker Publishing Research Risk Running Sleep Societies Sodium Specificity Spottings System Techniques Technology Test Result Testing Treatment Failure Urination Urine Use Effectiveness classification algorithm clinical decision support cost data exchange design experience health data hospital readmission improved interest machine learning method meetings mortality patient monitoring device patient population prevent programs prospective remote monitoring remote patient monitoring risk stratification sample collection sensor tool trend unsupervised learning urinary

项目摘要

Abstract Heart failure (HF) is the most common cause for both hospitalizations and readmissions in the Medicare program. HF’s high mortality rate and high hospitalization utilization rate via readmissions results in a large economic burden currently estimated at over $30B, and prevalence of HF is expected to continue rising by 46% by 2030. There is an opportunity to deploy remote patient monitoring (RPM) tools for measuring prognostic biomarkers of worsening HF and acute decompensation and hospital readmission. Bender Tech (BT) has developed a urine testing platform capable of easily attaching to a home-toilet for accurate and easy collection of longitudinal health and behavior data without the requirement of manual sample collection and/or testing. We propose to adapt our system for use in elderly HF patient populations by rendering data collection and transmission completely passive. We propose to integrate sensors and firmware capable of identifying when a user has used their home toilet (Specific Aim 1), develop the machine learning (ML) classification algorithms necessary for determining when to perform a testing sequence (Specific Aim 2), and use ML methods to demonstrate feasibility of user biometric identification via urinary testing data (Specific Aim 3). A successful outcome of this proposal will be a set of classification models built using ML techniques designed to enable passive collection of longitudinal urine profile data from a home-toilet for use in remotely managing elderly HF patients. This will ready the product for prospective clinical trials that would be the subject of a future phase II submission for use in remotely monitoring diuretic effectiveness and preventing hospital readmissions.

摘要心力衰竭(HF)是医疗保险计划中住院和再入院的最常见原因。心衰的高死亡率和再入院的高住院使用率带来了巨大的经济效益目前估计的负担超过300亿美元，预计到2030年，心力衰竭的患病率将继续上升46%。有机会部署远程患者监测(RPM)工具来测量预后生物标志物心力衰竭恶化、急性失代偿和重新入院。英国电信(Bender Tech)研发出一种尿液可轻松安装在家庭厕所上的测试平台，可准确、轻松地采集纵向健康数据和行为数据，而不需要手动样本采集和/或测试。我们建议调整我们的该系统用于老年心力衰竭患者人群，通过呈现完全被动的数据收集和传输。我们建议集成传感器和固件，能够识别用户何时使用了他们的家庭厕所 (具体目标1)，开发必要的机器学习(ML)分类算法以确定何时执行测试序列(特定目标2)，并使用ML方法演示用户生物识别的可行性通过尿检数据进行鉴定(具体目标3)。这项提议的成功结果将是一套使用ML技术构建的分类模型旨在实现纵向尿液剖面的被动收集来自家庭厕所的数据，用于远程管理老年心力衰竭患者。这将使产品做好准备预期临床试验将作为未来第二阶段提交的主题，用于远程监测利尿剂的有效性和防止再次住院。