1. CEC in the Era of AI and Big Data
Ori Ben-Yehuda, MD
Clinical Trials Center
Cardiovascular Research Foundation

2. Conflicts of Interest
None

3. Traditional CEC Process
Identify events from AE, SAE, and site reporting
Collect source documents
Review source documents
Create Narratives- identify key elements for adjudication
Committee meets to adjudicate- consensus achieved

4. Remote Adjudication (electronic)
Uploading of source documents to adjudication platform
Documents pre-reviewed and highlighted (optional)
Adjudicators log in, review and adjudicate
Concordance vs. Discordance among adjudicators determined by system – if discordant sent to third reviewer

5. Challenges in current system(s)
Slow, expensive, time consuming
Translations in international trials – further expense and delays
Face to face meetings- efficient and potentially less error prone, risk of being dominated by one member
Remote adjudications- risk of spurious concordance

6. Can machines learn?
Categorize or catalog people or things • Predict likely outcomes or actions based on identified patterns • Identify hitherto unknown patterns and relationships • Detect anomalous or unexpected behaviors ALGORITHMS are the key to Machine Learning

7. Potential implementation of AI in CEC
Natural Language Processing
Decipher charts and physician notes
Extract the useful information, analyze it, etc.
Translation

8. Why is machine learning applicable to CEC?
Large number of data points rather than large number of records
Data points are correlated
Potential to identify patterns which define an endpoint, which may defy easy logical rules
Very different though than current manual process

9. Types of Machine Learning
Supervised- set of examples; machine learns to replicate in other sets
Semi-supervised- not all examples given; machine learns to “fill in the blanks”
Too much data
Subtle data
Unsupervised- new patterns identified
Reinforcement Learning- only a set of rules defined (eg: artificial neural networks)
…….DEEP LEARNING

10. Validation of Machine Learning- can we trust the black box?
Not simply a black box that needs to be trusted
Can evaluate the output
Vis a vis traditional CEC
Do the results predict future events

11. What is Big Data? Not only large number of subjects
Large number of data points!
Examples: Actigraphy
Daily blood pressure
Daily diuretic dose
To adequately analyze all the myriad datapoints need significant computational capabilities- ie, machine learning

12. Challenges The output only as good as the data itself
GIGO!
Endpoints may differ from traditional endpoints
Requires different expertise