HCR.20.011 – AI-Driven Views in a Cardiovascular ADVICE Ecosystem

Route: Health care research, sickness prevention and treatment

Cluster question: 088 How can we predict, prevent, and treat cardiovascular diseases (atherosclerosis, heart failure, heart arrhythmia, and thrombosis) in individuals at an early stage?

In order to provide patient-centered care that is in line with patients’ preferences and needs, various treatment-decision support tools have been applied in the clinical practice over the past years. The advice from these tools is mostly based on group-level data from clinical studies in combination with generic treatment guidelines.
Within these support tools individual differences are largely neglected while evidence exists that patients’ psychological profile (e.g. distress, personality) and lifestyle behavior (e.g. smoking, physical activity, diet) are clearly associated with disease prognosis and outcomes for patients with chronic conditions such as cardiovascular disease.
The ADVICE research project will focus on exploring and developing an artificial intelligence based tool, in which patient profiles (demographic, clinical, psychological, and lifestyle) are taken into account to provide healthcare providers and patients an optimized personalized care plan advices.
Existing data of patients with Implantable Cardioverter Defibrillators (ICD) (N>1000) will be used to model and test new AI-based services. In addition, also new data in this population will be collected to 1)further train the AI application and examine how the data sources could be combined, 2)evaluate the usability and feasibility of the tool, and 3)evaluate the effectiveness of the tool on patient reported outcomes and healthcare costs. Furthermore, research will be done on ethics, privacy, and trustworthiness.
The findings from the study will provide a framework for development of personalized treatment plans for patients with chronic conditions. In addition, it will result in improvement of quality of life of ICD patients and significant health care cost decline due to a personalized approach.

Keywords

Artificial Intelligence, cardiovascular disease, decision support, patient reported outcomes, personalized health

Other organisations

Amphia Hospital, Eindhoven University of Technology (TU/e), Elisabeth-TweeSteden Ziekenhuis, Erasmus Medical Center (EMC), Harteraad, ONMI b.v., Secura b.v., Stichting Connect2move, UMC Utrecht (UMCU), University of Southern Denmark

Submitter

Organisation Tilburg University (TiU)
Name Dr. M. (Mirela) Habibovic
E-mail m.habibovic@uvt.nl