# Time Series Models of the Human Heart in Patients with Heart Failure: Toward a Digital Twin Approach

## TL;DR

This paper explores using digital twins and AI to model heart failure decompensation through wearable sensor data, aiming to improve personalized healthcare.

## Contribution

The paper presents one of the first attempts to model heart failure decompensation using time series data from a wearable monitoring system.

## Key findings

- Time series models of heart failure decompensation were developed using data from a wearable monitoring system.
- The study used data from the pilot phase of the SmartHeart study to explore digital twin applications in heart failure management.

## Abstract

Digital Twins (DTs) are digital replicas of physical entities. The use of DTs in healthcare is a growing area of research. With DTs, there is potential to revolutionize healthcare with the assistance of Artificial Intelligence. This can lead to achieving precision, personalization, and value addition in healthcare. Contributing to this field, we present one of the first attempts of uncovering time series models of decompensation of heart failure. This was performed using some of the first data collected from the pilot phase of the SmartHeart study, in which an at-home, wearable, wireless sensor-based digital self-monitoring system for people with heart failure was tested.