The mission of the Health Data Science group is to produce clinically actionable insights from observational health data by enabling data-driven healthcare. Improved interoperability of data is a necessary pre-requisite for this mission.

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We perform methodological research in clinical characterisation, population-level effect estimation, and prediction modelling. We develop open-source analytical tools that can be applied on the OMOP Common Data Model.

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We believe more education for young health data scientists, medical students, and healthcare professional, is needed to train them in the opportunities and limitations of big data in healthcare.

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Latest News

Thursday, January 18, 2024

The fifth European OHDSI Symposium "Scaling up reliable evidence across Europe" will be held in Rotterdam (NL) on June 3rd, 2024, again at an inspiring location, the Steam Ship Rotterdam.

The main symposium will take place on Monday, June 3rd, 2024 on the ship SS Rotterdam. Saturday, June 1st, and Sunday, June 2nd will be dedicated to workshops and workgroup meetings, to be held in the Education Centre of the Erasmus University Medical Center, Rotterdam, The Netherlands.  

For more information, registration, and call for participation: click here.

Hope to see you all there!


Tuesday, September 26, 2023

The Medical Informatics Europe took place this year in Gothenburg, Sweden. Department members Aniek, Cynthia and Ross all attended. Aniek gave a presentation on “Challenges of estimating global feature importance in real-world health care data” and Cynthia gave a presentation titled “Does Using a Stacking Ensemble Method to Combine Multiple Base Learners Within a Database Improve Model Transportability?”. Ross had a poster detailing the work that has been performed for the first release of the DELPHI prediction model library. 

The conference had several thousand delegates and covered a wide range of informatics topics, from technology to be implemented at the bedside, to analysis of real-world data. It was clear that there is lots of motivation for generating better machine learning and AI focused tools for implementation into routine healthcare.

One thing of note is that Ross chose to follow the Erasmus MC green initiative and went to Gothenburg by train. This journey was spread across 2 days and involved 12 hours of train travel with a stop in Copenhagen and Hamburg. This trip comes highly recommended by Ross.