Artificial Intelligence and Clinical Trials: Interventions and Cycles
September 24, 2019
OVERVIEWExamining the various points in a clinical trial life cycle where machine learning can "intervene": from data science, to trial design to patient matching and site selection. Such changes can then impact the cyclical nature of trials as one trial leads back to a subsequent phase of the same study.
WHAT YOU'LL LEARN
- Understanding the different terms and processes of machine learning
- Overview of current landscape of machine learning in clinical trials
- Where machine learning can play a role
- Identifying the stakeholders that can benefit from machine learning
- Challenges to, and insights from, machine learning adoption
WHO SHOULD ATTENDAnyone working in clinical trials, including sponsors/investigators, contract research organizations, patient advocates, real-world evidence scientists.
WHAT ARE THE BENEFITSUnderstanding the current uses of machine learning and where it can improve clinical trials.
MEET THE INSTRUCTOR
David Hadley, PhD, Director of data Quality and AnalyticsDavid has a background in computer science, artificial intelligence and epidemiology, and has been researching the genetic epidemiology of various traits and diseases for over a decade on both sides of the Atlantic. At Inteliquet, David utilizes the volume of data to help drive improvements in clinical data quality for oncology clinical trials and leads their scientific collaboration and research team.
EARN CEUsParticipants are eligible to receive CEUs upon attendance and successful completion of a web-based assessment within 30 days after the webinar. CEUs are not granted after the 30-day assessment deadline.
SCDM is authorized by IACET to offer 0.2 CEUs for this program.
|Group (max. 10 ppl.)||$960||$1,140|
Group Registration Policy: Any group registration allows up to 10 people to attend the webinar and to complete the webinar assessment. Credits will be granted on an individual basis upon successful completion of the assessment by the registered participants.
Refund Policy: Participants will receive a full refund if notice is provided in writing via post or email one week prior to the webinar date. If cancellation occurs within the week prior the webinar, participants will be allowed to apply 50% of the webinar fee to the next offering of the same webinar. No refunds will be offered after that time.
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