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Created page with "Project Sparrow is one of ROEB's Transformation initiative which started in April 2021. The project aims to create a digital library of online data enabled by web scraping tec..."
Project Sparrow is one of ROEB's Transformation initiative which started in April 2021. The project aims to create a digital library of online data enabled by web scraping technologies of emerging health products, techniques, technology, processes, trends etc, for the purpose of informing Compliance &Enforcement decisions. We are currently in a research phase - defining the problem statement, discovering what other partners have done in this space. The project anticipates taking my pivots as new information is discovered.
== Project Context ==
ROEB primarily uses internal collected data to inform its C&E decisions. With the dawn of the age of the internet and social media, there are numerous success stories in industry that capitalizes on publicly available data to inform business decisions - as the saying goes : data is the new gold. ROEB would like to explore the use of public source of data to further refine, inform, or validate current C&E decisions.
The scope of the project is currently being refined.
== The Waterloo Experience (WE) Story ==
An opportunity was presented to Health Canada to be part of the Waterloo Experience - a program which provides students in-demand Artificial Intelligence (AI) and business skill development (co-created with AltaML, Microsoft and Industry/Public partners), team-based project experience, and real-world industry exposure. Health Canada is one of the partners that will bring a business problems to these students so they can apply AI and ML to solve them.
The Problem: Site Risk Profiling.
Decisions informed with data that is driven by risk is a prime focus for all programs in ROEB. Health Canada is proposing to use Site Risk Profiling as the use case for this opportunity. Existing approaches to Site Risk Profiling is being conducted with HC data applied with multi-variant statistical analysis. It is unknown, however, if this approach has biases or gaps that can create risk to the compliance and enforcement mandate. To validate Health Canada’s approach to Site Risk Profiling, we are proposing to leverage public data via Twitter to assess Machine Learning potential and compare our approach to a solution provided by WE Accelerate.
== Repository ==
Click [https://022gc.sharepoint.com/:f:/s/ROEB-POD-TransformationOffice/Eu6lHpqoNrNPtE94pW7HIK4BxqktGLbTq-7E6CZeH4ZFSw?e=486tus here] to access the Sparrow files.
== Project Context ==
ROEB primarily uses internal collected data to inform its C&E decisions. With the dawn of the age of the internet and social media, there are numerous success stories in industry that capitalizes on publicly available data to inform business decisions - as the saying goes : data is the new gold. ROEB would like to explore the use of public source of data to further refine, inform, or validate current C&E decisions.
The scope of the project is currently being refined.
== The Waterloo Experience (WE) Story ==
An opportunity was presented to Health Canada to be part of the Waterloo Experience - a program which provides students in-demand Artificial Intelligence (AI) and business skill development (co-created with AltaML, Microsoft and Industry/Public partners), team-based project experience, and real-world industry exposure. Health Canada is one of the partners that will bring a business problems to these students so they can apply AI and ML to solve them.
The Problem: Site Risk Profiling.
Decisions informed with data that is driven by risk is a prime focus for all programs in ROEB. Health Canada is proposing to use Site Risk Profiling as the use case for this opportunity. Existing approaches to Site Risk Profiling is being conducted with HC data applied with multi-variant statistical analysis. It is unknown, however, if this approach has biases or gaps that can create risk to the compliance and enforcement mandate. To validate Health Canada’s approach to Site Risk Profiling, we are proposing to leverage public data via Twitter to assess Machine Learning potential and compare our approach to a solution provided by WE Accelerate.
== Repository ==
Click [https://022gc.sharepoint.com/:f:/s/ROEB-POD-TransformationOffice/Eu6lHpqoNrNPtE94pW7HIK4BxqktGLbTq-7E6CZeH4ZFSw?e=486tus here] to access the Sparrow files.