Andre Dekker

Prevention session

14.30-16.00

Profile

Prof. Andre Dekker, PhD is a board-certified medical physicist at MAASTRO Clinic and full professor at Maastricht UMC+ and Maastricht University where he holds the chair “Clinical Data Science”. 

His research focuses on three main themes:  

1) building global data sharing infrastructures; 

2) machine learning cancer outcome prediction models from this data; 

3) applying outcome prediction models to improve lives of cancer patients.  

The main scientific breakthrough has been the development of a Semantic Web and ontology based data sharing and distributed learning infrastructure that does not require data to leave the hospital. This has reduced many of the ethical and other barriers to share data. 

Prof. Dekker has authored over 100 publications (h-index 48) in peer reviewed journals covering informatics, imaging, radiotherapy, tissue optics and heart disease and holds multiple awarded patents. He has held visiting scientist appointments at the Christie Hospital NHS trust; University of Sydney Australia; Liverpool and Macarthur Cancer therapy centres Australia; Illawarra Shoalhaven Local Health District Australia; Universita Cattolica Del Sacro Cuore, Italy; Radiation Therapy Oncology Group, USA, Varian Medical Systems, USA and the Princess Margaret Hospital in Canada. 

For more information, visit his LinkedIn page.

 

Citizen-centred big data for health care, prevention and research – The Personal Health Train 

Health care is in transition. Predictive, preventive, personalized and participatory (P4) medicine, eHealth and value-based learning health systems are considered the future of health & well-being in our digital society. Exploding care costs further substantiate the need for a change in the way our healthcare system works and how interventions are applied. However, the infrastructure supporting this transition is largely lacking. In the Netherlands, like many other countries, health data are fragmented, and citizens lack the tools to take meaningful control over their own data and health. 

The personal health train (PHT) addresses this societal need by creating an infrastructure for the (re)use of personal health data in health care, prevention and research. The train metaphor is used to explain the infrastructure: stations with health and health-related data are connected by secure and monitored tracks via which care professionals, researchers or citizens can run trains that ask questions to the stations and return answers. Bringing questions to data rather than moving data is a key differentiator of the PHT and avoids legal, ethical, societal and technical barriers associated with physical data sharing. 

The PHT is supported by a community guided by principles - the PHT manifesto. PHT’s main goals are to empower citizens to control access to their personal data for prediction, treatment and prevention, to provide dynamic consent to the use of their data in research, to partner with healthcare providers in shared decision making, and to support citizen & patient-initiated research. It respects the autonomy and privacy of citizens and thus implements important aspects of both responsible science and open data.