Sign in
Ctrl K


Using big data to put a cardiovascular digital twin into the hands of people

MyDigiTwin is a scientific initiative to develop a platform in which individuals can, for the first time, directly compare their personal health data with big-data reference data available from multiple cohorts, including ±200,000 national and ±500,000 international volunteers with long-term follow-up. This big-data will be queried via FAIR data access points and “on the fly” comparison by Artificial Intelligence (AI)-based algorithms will return results that will be used to render a “Digital Twin” (representative) of each individual user. This “Digital Twin” informs the user on the actual observed cardiovascular events of the identified “most alike” volunteers from big-data reference sets. The “Digital Twin” can also be modified for input variables to allow the user to simulate the effect of changes (e.g. in Lifestyle) and assess benefits or harm. The MyDigiTwin platform will also make AI tools available to the users to “check” whether their personal data, including possible pharmacotherapy, is in line with the official recommendations from professional cardiovascular practice guidelines. Dedicated research will be directed to obtain fundamental insights into the readiness of society to optimally align the foreseen platform and cockpit (user interface) with the needs and expectations of users. A co-creation strategy will be used to develop MyDigiTwin and its efficacy, in order to empower patients and enable shared decision-making. This will be tested in a real-life setting by our clinicians. MyDigiTwin contributes to self-management and improved interaction with health care professionals through knowledge-driven empowerment, technical solutions to enhance communication, and reports to simulate shared decision making.

Participating organisations

Netherlands eScience Center
University Medical Center Groningen
Life Sciences
Life Sciences


P. van der Harst
Principal investigator
University Medical Center Groningen

Related projects


Improving the vantage6 federated learning ecosystem

Updated 10 months ago
In progress


Coronary artery disease: risk estimations and interventions for prevention and Early detection –...

Updated 15 months ago
In progress

Perfect Fit

Targeting key risk factors for cardiovascular disease in at-risk individuals using a personalized and adaptive approach

Updated 9 months ago
In progress


Self tracking for prevention and diagnosis of heart disease

Updated 15 months ago
In progress


Fusible evolutionary deep neural network mixture learning from distributed data for robust medical...

Updated 14 months ago

SecConNet Smart

Smart, secure container networks for trusted big data sharing

Updated 18 months ago

Genetics of sleep patterns

Detecting human sleep from wearable accelerometer data without the aid of sleep diaries

Updated 4 months ago