Check out the event dedicated page for a summary of the day.

The BBS is pleased to host a full-day seminar on predictive modelling, machine learning, and causality with several eminent speakers. The talks will present recent methodological advances and challenges as well as case studies from the pharmaceutical industry and academia. We welcome all quantitative scientists to this event which will be a great opportunity to meet with colleagues and exchange ideas on this emerging and vibrant field.

The seminar is free of charge but registration is mandatory for organizational reasons. Please register via email to by Friday, October 18, 2019, the latest.

Date: Friday, November 1st, 2019, 08:30-16:45

Venue: Auditorium Roche Builing 683, Viaduktstrasse 31-35, Basel

Full program in pdf – with abstracts

Meeting agenda in pdf

Slide decks

Ewout Steyerberg, Clinical prediction models in the age of artificial intelligence and big data

Willi Sauerbrei, The EQUATOR networkand guidelines for prediction models

Torsten Hothorn, Score-based transformation learning

Peter B├╝hlmann, Causal regularization for distributional robustness and replicability

Giusi Moffa, Predicting putative intervention effects after causal structure learning from survey data

Andrew Shattock, Using machine learning and disease models to evaluate target product profiles of novel interventions (No slide deck available)

FedericoMattiello, Identifying high-risk patients in Non-Hodgkin lymphoma (and trying to get insights into the disease biology)

Mark Baillie, Novartis benchmarking initiative: making sense of AI

Chris Harbron, Experiences from running internal prediction challenges within a pharmaceutical company

Sorry, the comment form is closed at this time.