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HJERTERO (”Calm Heart”) – Artificial Intelligence to Detect Heart Patients with an Increased Risk of Anxiety and Depression

Heart patients have twice as high risk of developing anxiety and depression compared to patients without a heart disease. Hence, there is significant potential in exploiting technological opportunities for early detection of signs of anxiety and depression in heart patients – to the benefit of both quality of life for the patient and socio-economic aspects.

Artificial intelligence will help us prevent anxiety and depression in heart patients.

Heart patients often develop anxiety and depression

In 2020 up to 500.000 Danes will be heart patients, and half of us can expect to have a heart disease by the time we reach the age of 55.

Having a heart disease can lead to psychological disorders, as the risk of developing a depression is twice as high in heart patients compared to people without heart disease.

When heart patients with symptoms of anxiety and depression do not receive psychological support, they are affected both physically and mentally. The interrelation between physical and mental health has a significant effect on the treatment and prospects. The mental state of health affects the patient’s ability to follow heart rehabilitation, including important lifestyle changes. The costs related to depressions in Denmark are estimated at 14 bio. kr./year, while anxiety-related costs reach 6 bio. kr./year. And on top of that are the immeasurable effects of going through anxiety and depression for the individual patients.



The ’HJERTERO’ project will utilise the unique national patient registries in Denmark to develop a data-driven prediction model to support detection of patients with coronary heart disease who are at increased risk of developing anxiety or depression. We will develop a solution that considers both patient preferences, opportunities and barriers, while also providing a tool to support the healthcare staff’s daily clinical operations. We will help the many patients at risk of mental complications, and the solution will support equal access to healthcare by ensuring the same diagnostics and support independent of where the patient lives.

Purpose and Solutions

The purpose of developing and validating a data-driven prediction model is to enable preventative initiatives when patients are at high risk of developing anxiety or depression.

At the same time, the purpose is to carry out a Proof-of-Concept when it comes to clinical use of the final model prototype as well as to provide recommendations for implementation and upscaling.

Success Criteria

  • Data-driven prediction model developed based on identification of data
  • Prediction model validated through two cohorts (w/ and W/out anxiety/depression)
  • Proof-of-concept – prototype tested in clinical departments and staff interviewed
  • Evaluation and recommendations based on qualitative and quantitative assessment
  • Recommendations for implementation and upscaling for other disease areas with risk of depression and anxiety (e.g. diabetes and COPD).

Partners & Supplier

  • The Region of Southern Denmark:
    • The Health Innovation Centre of Southern Denmark, Region of Southern Denmark
    • Odense University Hospital
    • Department of documentation and management
  • University of Southern Denmark:
    • Department of Health
      Maersk McKinney Møller Institute
      Research Unit for General Practice
  • SAS institute
  • Aalborg University
  • Zealand University Hospital, Roskilde.


Project period: January 2021 – December 2023

Funding: The project is one of 13 signature projects on artificial intelligence in municipalities and regions appointed in the National Financial Agreements for 2021.


Carsten Jensen


Brugercentreret Innovation

24 96 11 72 Carsten Jensen på LinkedIn