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Big data for better cancer care 01 Nov 2018

Big data for better cancer care
Big data, artificial intelligence, machine learning and data science are expected to have a major impact on
day-to-day cancer practice. Big data based services such as automated image segmentation, radiomics,
decision support systems and literature mining are products already available to the cancer community
and these are expected to rapidly change the way we practice medicine.

Since 2008 Maastricht University and MAASTRO Clinic have developed a research program on this topic. A
global IT infrastructure has been developed in which cancer centres are being connected with currently up
to 25 partners. The aim is to enable cross-institute, privacy-preserving, data sharing and machine learning
and more efficient clinical evidence generation: a concept now commonly referred to as “Rapid Learning”.

In the lecture innovative technology to extract, store and process (big) data for Rapid Learning is

All this data is often seen as tremendously promising and is predicted to change health care
radically, but at this point in time is mostly a challenge as we keep accumulating data without a clear path
to clinical applications while privacy concerns are on the rise. Methods and examples of how we go from
data to making a difference in lives of cancer patients are presented, as are the methods to do this in a
way that preserves the privacy of patients such as the Personal Health Train and distributed learning.

1 CPD credit.
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Duration:50 mins

Speaker info

Andre Dekker

Professor André Dekker Professor of Clinical Data Science, Maastricht University Professor André Dekker’s main work aim is to be a scientist. He is a full professor of Clinical Data Science at Maastricht University and leads the GROW-Maastricht University research division of MAASTRO Knowledge Engineering and Academic IT at the Maastricht University Medical Centre. 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. His team’s scientific breakthrough has been the development of a 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 sharing health data. They have shown this approach works in more than 20 cancer centers worldwide. Next to his research activities, he is a board certified Medical Physicist and Manager of Research and Education of MAASTRO Clinic, one of the leading radiation oncology centers in Europe. His department has more than 40 full-time staff and he is a member of MAASTRO’s executive board. He has been the Head of IT, Head of Medical Physics and undertook his PhD in cardiac surgery.