This episode features Dr. Adam Hulman, a mathematician with a PhD in epidemiology who specialises in bridging the gap between computer science and clinical research. Adam discusses the significant challenges involved in developing prediction models that actually work in real-world healthcare contexts, particularly in diabetes management.

The conversation covers machine learning applications in medical settings, the complexities of training models with medical data, and the importance of clinical validation. Adam also shares insights about his research methodology, including his use of the LEGO Serious Play approach in scientific work. 

Adam's unique dual expertise in mathematics and epidemiology provides valuable perspectives on both the technical and practical aspects of healthcare data science.

USEFUL LINKS:

More about Adam Hulman: https://www.au.dk/en/[email protected]/

Steno Diabetes Center Aarhus: https://www.stenoaarhus.dk/kontakt/Adam-Hulman/

LEGO Serious Play: https://www.lego.com/en-us/themes/serious-play/background

Tripod: https://www.tripod-statement.org/