About

The Translational Data Science & AI (TDS) Lab is headed by Prof.dr. Marco Spruit and is primarily located at the Health Campus The Hague within the LUMC department on the third floor, but also has a BioScience Park Leiden office in the Gorlaeus building on the second floor. We are an energetic and innovating team with an AI mindset that participates in many Dutch and European research projects and is actively involved in developing and teaching novel AI and machine learning course modules throughout the Leiden University Medical Center (LUMC).

Mission Statement

The TDS Lab's mission is to connect practical problems in healthcare practices to fundamental challenges in data science and to subsequently address both simultaneously. This is our encompassing Translational Data Science (TDS) research theme, which bridges the best of both worlds. Pasteur's Quadrant in the figure below visualises our drive to achieve a better fundamental understanding of the world around us through data science innovations by being societally inspired, demand-driven and solution-oriented.

Strategic objectives

1. Establish an authoritative and open national infrastructure for Dutch health research, education and care to accelerate innovation and to democratise data science technologies through especially natural language processing and automated machine learning technologies, through
   1. [ELAN] Professionalising the ELAN infrastructure
   2. [FML] Developing federated learning techniques for EHR data
   3. [GenAI] Generating synthetic EHR data for digital twins
   4. [MLOps] Deploying clinical decision support systems in healthcare practices
   5. [NLP] Showcasing the Welzijn.AI bot for the vulnerable elderly
2. Modernise education in Dutch healthcare for students and professionals with artificial intelligence and machine learning-focused modules
3. Communicate our findings and working prototypes to a broader audience through embedded applications on the tdslab.nl website

Translational data science in Pasteur's Quadrant (on the left) combines basic data science understanding with applied data science use considerations.