Precision medicine typically refers to the development of drugs and other interventions for individual patients. But how do you assess efficacy and make predictions in this extreme small data regime?

Bayesian framework is ideal for this type of inference as it allows us to combine population and personal effects in a principled way and make predictions for both groups and individuals. The inferences are further improved when we introduce mechanistically inspired components into the modeling framework.

In this talk, I will provide an overview of these types of models using three real-world examples:

1) Pharmacokinetic model suitable for an early stage trial in small patient populations, particularly in rare diseases

2) Semi-mechanistic joint survival model for targetted therapies in solid tumors

YOU MAY ALSO LIKE:

• Real-time Systems with Spark Streaming and Kafka (in London on 30th September - 1st October 2019)
• CloudNative London 2019 (in London on 25th - 27th September 2019)
• Keynote Evening by Brian Ketelsen! (in London on 17th October 2019)
• Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
• ProgNET London 2019 (in London on 11th - 13th September 2019)
• You got served: How Deliveroo improved the ranking of restaurants (in London on 27th August 2019)
• AlphaGo Zero: Mastering the Game of Go without Human Knowledge (in London on 27th August 2019)
• Genetic Programming for RL on Minecraft (SkillsCast recorded in August 2019)
• Designing trust and transparency using AI (SkillsCast recorded in August 2019)