Key result
Wearable self-monitoring data informs initial time series models to analyze HF decompensation.
Why the study?
Digital Twins powered by Artificial Intelligence have the potential to enable precision and personalization in healthcare, but time series models capturing heart failure decompensation remain largely unexplored.
Wearable sensor data can be utilized to develop time series models of heart failure decompensation, representing an early step toward digital twins in cardiology.
Wearable sensor models may aid HF self-monitoring; leaves open validation of digital twins for decompensation prediction.
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.
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Wickramasinghe et al. (2025) studied this question. The study presents initial time series models that analyze heart failure decompensation using data from a wearable sensor-based system for self-monitoring.
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