Please note that all agenda timings are Eastern time.
For PDT time, please download the program here.
Data Driven Discovery – Optimizing Research Through Data
9:00 am Panel Discussion – Challenges with Data: Validation, Access, Quality & Real-World Applications
Synopsis
• Addressing current obstacles and exploring the most common challenges with data quality and validation
9:45 am Case Study – Talking Data – Using ML to Automate Coding of Concomitant Medications
Synopsis
• Ensuring consistency in the collection of quality data
• Perspectives on the project and outcomes of using ML so far
• Strategically setting yourself up for success and managing unexpected challenges
10:15 am Morning Break & Networking
11:00 am Case Study – Dirty Data in a Black Box: Uses, Abuses and Misuses of AI/ML Technology in Drug Development
Synopsis
• Solving problems associated with the data quality and integrity
• Identifying areas of drug discovery where AI/ML could be applicable
• Interrogating datasets – examining applicable algorithms
11:30 am Case Study – Using AI to Predict the Disease-Gene Combinations
Synopsis
• Goal: Predict today which diseases and gene targets will reach the clinic in 5 years
• Examining technologies involved in terms of data sources, data mining, predictive models and cloud computing
• Identifying challenges and lessons learned throughout the project
12:00 pm Networking Lunch
Strategic Thinking to Overcome Major Challenges of Using AI
1:00 pm Panel Discussion – The Black Box Problem – Explainability, Interpretability and Reproducibility of Machine Learning and AI: Concepts to Build Trust Towards AI/ML
Synopsis
• Discussing advancements over the last few years tackling the black box problem and exploring work that still needs to be done to continue the adoption of AI in healthcare
1:45 pm Case Study – From Discovery to Development – Designing Better Trials Using Real-World Evidence and Clinical Intelligence
Synopsis
• Enabling clinical intelligence to empower development of precision trials with high enrolment and low attrition rates
• Leveraging RWE from driver analytics 1000s of trials representing multiple diseases and millions of patients to design better trials
• Using real-world data to improve patient engagement in clinical trials
2:15 pm Strategies for Advancing the Use of AI/ML to Evaluate Digital Health Technologies (DHTs) for Drug Development: Case Study in Parkinson’s Disease
Synopsis
• Identifying key obstacles in deploying DHTs for drug development with a focus on analytic approaches
• Methodology to evaluate the maturity of DHT analytic approaches based on the patient’s perspective for the purpose of regulatory drug development
• Perspective on AI/ML approaches for Parkinson’s drug development based on broad learnings from regulatory engagement regarding DHTs
2:45 pm Afternoon Networking Break
3:30 pm Afternoon Roundtables
Synopsis
Following the day’s talks and discussions, the audience will split into groups, selecting one of the below topics for a knowledge exchange session. Each session will last 45 minutes for audience members to discuss:
• AI and Machine Learning Approaches in Clinical Trials
• Overcoming the Black Box Problem
• Preparing for the Next Wave of Innovation – Quantum Computing