COPD exacerbations management

Machine learning methods based on Dynamic Bayesian Networks, suitable for modelling knowledge and handing time series data, are added to the Ecosystem Transaction Space to implement apps that predict exacerbations and avoid hospitalizations. These apps will be built on top of advanced wearable monitoring KETs, available in the GK Things Catalogue, that combine, in a single wearable garment piece, time series data for blood pressure, pulse oximetry, ECG, respiration, skin temperature and activity.

Target population:

Both Aragon and Puglia plan on including COPD 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, oxygen saturation, 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. 

A key requirement in both sites will be the integration of these KETs, and the data flows associated with them, into the current regional systems while complying with all the regulatory standards and being reliable, safe and robust.

 

Aragon (Spain)

Puglia (Italy)