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

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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.