US CSS Workshops

Please note the following Workshops are subject to change. 

 

Registration for the Workshops is now open, click here to register. 

If you have already registered for the CSS conference and wish to attend a Workshop, please email the PhUSE Office.

Safety Analytics

Sunday, June 9th - 7.00-9.00pm

Room: Fenton

Mary Nilsson, Eli Lilly

THIS SESSION WILL BE RECORDED

The Analysis and Displays White Papers project team has developed six white papers outlining recommendations for analyses and displays for clinical trial data common across therapeutic areas (e.g. adverse events, labs, vital signs, concomitant medications). These white papers cover recommendations for individual study and integrated analyses. While the white papers include a rationale for the recommended analyses and displays across various choices, some topics would benefit from additional cross-functional education on safety analytic principles and a more thorough rationale.

This workshop will cover common pitfalls and questions when analysing safety data from clinical trials.  The material will be presented in a manner appropriate for a cross-functional audience (e.g. medical, medical writers, regulatory scientists, statisticians, statistical programmers). This workshop will provide an opportunity for attendees to gain a greater understanding of the recommendations, improve their expertise in safety analytics, and debate alternatives. Attendees are encouraged to read the following four white papers from the PhUSE Deliverables Catalog as a pre-read:

Let's Make a Knowledge Graph! An Interactive Hands-on Workshop

Sunday, June 9th - 7.00-9.00pm

Room: Ellsworth A&B

Tim Williams, UCB

Knowledge Graph is a term that is gaining popularity to describe multi-dimensional graph databases that use a reasoner to infer knowledge from data. It sounds complex, but at its core is a very simple way to join data together using meaningful relationships. F.A.I.R. data (https://www.go-fair.org/fair-principles/) is built on these Linked Data concepts and it can provide future-proof data for pharma, breaking down traditional data silos in a highly inter-connected, extensible way.

This is an updated version of previous workshops at the US CSS and EU Connect conferences. We invite attendees who have not participated in the previous workshops to experience Linked Data in this interactive session. You will use a web application to diagram relationships for clinical trial processes and data, then convert your whiteboard drawing to Resource Description Framework (RDF). You will then query the data, using an ontology to infer values and relationships not in your original content. As a last step, you will seamlessly merge your study with data from all the other attendees.

This introductory workshop provides the background you need to launch your own exploration of this technology or to participate in a PhUSE Working Groups project. We welcome attendees with no previous experience with Linked Data.

Pre-registration is required. You must bring a laptop with remote desktop capability and attend a preparatory webinar in the days preceding the workshop.

Educating for the Future: Community Engagement - Design Thinking Focus Group

Sunday, June 9th - 7.00-9.00pm

Room: Spring

Ian Fleming, Medidata      James McDermott, Achieve Intelligence

                   

The Educating for the Future Working Group is looking for volunteers to help us better understand the community and their learning objectives. This session will have you speaking with members of the Working Group in an interactive workshop designed to help them build empathy with the people we are serving, the PhUSE community.

Anyone who is interested in guiding the future of the Working group or who is interested in further education would be welcome to attend. We expect the session to have lively and fun discussions in an open and free environment that has us all working towards a more robust future.

 

Machine Learning Programming

Monday, June 10th - 6.30 - 8.30pm

Room: Great Hall B

Sairam Gorthi, J&J       Kevin Lee, Clindata Insight       Sam Tomioka, Sunovion

               

The Machine Learning Programming workshop is intended for statistical programmers and statisticians who want to learn how to conduct simple machine learning projects. The Machine Learning Programming workshop will go through the following simple steps:

  1. Identify the problems to solve.
  2. Collect the data.
  3. Understand the data by data visualisation and metadata analysis.
  4. Prepare data – training and test data.
  5. Feature engineering.
  6. Select algorithm.
  7. Train algorithm.
  8. Validate the trained model.
  9. Predict with the trained model.

This workshop will use the most popular machine learning program – Python – and the most popular machine learning platform, Jupyter Notebook/Lab. During the workshop, programmers will see actual Python codes in Jupyter Notebook to run simple machine learning projects. Programmers will also be introduced to popular machine learning modules – pandas, numpy, scikit-learn, TensorFlow and Keras.  

 

 

 

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