Big Data and Stroke

Course Contributors:

Dr. Adriana Conforto, Dr. Ben Bray, Dr. Sook Lei Liew, A/Prof. Monique Kilkenny, Dr. Mateus Freitas, Dr. Xiaoquian Jiang, Dr. Bindu Menon, Dr. Caleb Ademola Omuwa Gbiri, Dr. Alexis Simpkins

Course Description: 

This course, developed under the leadership of Dr. Adriana Conforto, co-chair of the WSO Research Committee, is designed to provide participants with a comprehensive understanding of leveraging big data in the field of stroke research and healthcare. The modules cover a range of topics from open science initiatives to ethical considerations, integrating computer programming and data science skills, and translating big data insights into clinical practice. Participants will gain practical knowledge and skills necessary for conducting or joining impactful big data projects in the context of stroke.

Course Learning Objectives:

Upon completing the course, participants will be able to:

• Interpret and communicate big data insights to healthcare professionals.
• Integrate data-driven approaches into clinical decision-making.
• Understand the principles of open science and its application in stroke research.
• Foster collaboration and open communication in scientific workflows.
• Describe common principles for computer programming.
• Understand how computer programming can improve reproducibility in stroke research
• Understand sources of bias and strategies to decrease bias in observational studies.
• Comprehend ethical considerations in the use of big data in stroke research.
• Implement privacy protection measures for sensitive health data.
• Understand the principles of machine learning and deep learning.
• Reflect on personal experiences and challenges in conducting big data projects.
• Discuss strategies for overcoming obstacles and achieving project goals.