Artificial Intelligence and Machine Learning hands-on workshop for Clinical Data Management (Part 1 & 2)

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Monday, September 14, 2020: 11:00 AM - 12:15 PM
/ CEU Credits:0.4

SESSION CHAIRS & SPEAKERS

Workshop Instructor
Ashley Howard
Pfizer
Workshop Instructor
Malaikannan Sankarasubbu
Saama Technology
Workshop Instructor
Prasanna Rao
Head, Artificial Intelligence and Data Science
Pfizer

SESSION DESCRIPTION 

Artificial Intelligence and Machine Learning (ML) are over-hyped with very high expectations from various stake holders in the data management industry. This workshop is designed for all types of users who manage clinical trial data, whether you are a site investigator or a data manager or a clinical data scientist or a technology consultant, we will show you how Machine Learning models were trained for a data management use case. Many of you have attended several conferences to hear about the power of AI in presentations but never get to experience first-hand how these expert systems are designed, trained and predict to help humans in reconciling data. This workshop is organized as follows:

  • Pre-conference material: We plan on crowd sourcing CRF (mock) data, workshop participants will be provided instructions on how to create sample CRF data which will have to be completed a few days in advance of the workshop. The idea is to use this dataset to demonstrate how AI systems can predict discrepancies in the data.
  • Part-1 (on-demand): This virtual workshop will first describe the pain points in data reconciliation, define the use case, describe how it was designed, discuss types of Machine Learning models utilized, as well as the methodology we followed when data managers trained the machine.
  • Part-2 (Live Stream): Prior to part-2 we will upload your data into the AI prediction system, predict discrepancies with your crowd sourced CRF (mock) data and during the live-stream session our experts will analyze accuracy results for this dataset.

Workshop will be very informative, and we encourage your participation by providing us sample CRF data to explain and elaborate on how AI systems learn and predict. At the end of part 2, you will have a good in-depth understanding of how human/machines can work in tandem to reconcile data.


TOPIC


LEARNING OBJECTIVES

- Practical use of AI in data management.
- Methodology of Machine Learning and how to be ready to innovate and transform your Org.
- In-depth analysis of ML model predictions.
- How to upskill resources and be ready to embrace innovative technologies?

SESSION INFORMATION

CDM Certification Competencies::
Electronic Data Collection,CRF Design,Data Query Processing and Tracking

Target Audience:
Data Managers, Clinical Scientists,Site Investigators, CRAs etc,Technologists and others