Transforming Medicine Through AI-Enabled Healthcare Pathways

Explore the potential of AI in revolutionizing healthcare pathways through this comprehensive lecture. Delve into the challenges and opportunities presented by machine learning in medicine, focusing on cancer as a case study. Examine the complexities of healthcare data, patient life events, and counterfactual outcomes. Learn about the collaborative efforts between machine learning experts and cancer data collection services to develop near-real-time analysis methods for patient-level data. Discover a system-theoretical approach to healthcare that aims to deepen medical understanding and promote interdisciplinary collaboration. Cover topics such as cancer registries, personalized medicine, deep learning, recurrent neural networks, and clinically actionable models. Gain insights into data quality, automated curation engines, and the concept of temporal thermotyping. Understand the current state of machine learning in healthcare and explore its future potential in transforming medical practices.

Intro
Cancer registries
Building a whole care pathway
Breaking data standards
Data quality
Data collection
Stacked bar chart
Automated curation engines
Personalized medicine
Why ML isnt used today
What is happening today in ML
State transitions
Deep learning
Recurrent neural networks
Clinically actionable models
Pass
Temporal thermotyping
Example
Assumptions