Predicting readmissions and decompensations in HF

Telemonitoring services and machine learning with Dynamic Bayes Networks will be harnessed to implement an advanced model for predicting acute HF decompensations, taking comorbidities into account. Building on the experience of the Multisensor Monitoring in Congestive Heart Failure (MUSIC) Trial, GK Healthcare Space apps allow to explore which other longitudinal data (measured by GK Consumer Space “things” , e.g. bio-impedance, heart rate, respiratory rate and volume, physical activity duration and intensity, body posture, gathered with a wearable platform as the one depicted in can be used for predicting decompensations.

Target population:

Both Aragon and Puglia plan on including HF patients 65+ years, participating in already existing regional programs that would be enhanced by GK’s machine learning prediction models and a set of wearables.

Key enabling technologies:

  • Wearables/medical devices, intended to monitor and track key variables such as physical activity, weight, blood pressure, heart rate or SpO2. 
  • Professional’s online platform/dashboard, providing overview of alarm signs and/or relevant information. 
  • App for smartphone or tablet, enabling self-management, healthy lifestyle promotion, and regular follow up of patients through their interaction with a digital coach or chat-bot.

 

Aragon (Spain)

Puglia (Italy)