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.

抽象的心力衰竭（HF）是医疗保险计划中住院和再入院的最常见原因。 HF的高死亡率和高住院利用率通过再入院率导致了巨大的经济目前估计的烧伤估计超过30B，到2030年，HF的患病率预计将继续上升46％。有机会部署远程患者监控（RPM）工具，以测量 HF和急性代表和医院再入院恶化。 Bender Tech（BT）开发了尿液测试平台，能够轻松连接到家庭用品，以准确易于收集纵向健康和行为数据，无需手动样本收集和/或测试。我们建议适应我们的通过使数据收集和传输完全被动地用于HF患者人群中。我们建议整合传感器和固件，能够识别用户何时使用其家庭厕所（特定目标1），开发机器学习（ML）分类算法，确定何时执行测试序列（特定的目标2），并使用ML方法证明用户生物特征的可行性通过尿测试数据识别（特定目标3）。该提案的成功结果将是一组使用ML技术构建的分类模型，旨在使纵向尿液概况的被动收集来自家庭用品的数据用于远程管理老年HF患者。这将为产品准备前瞻性临床试验将成为未来II期提交的主题，用于远程监控利尿剂有效性并防止医院再入院。