Introduction of Rising Researchers

This edition highlights students who participated in the eight-week virtual research intensive. The course is designed to provide you with an introduction to statistical and machine learning techniques for understanding, finding, and explaining health equity gaps in electronic health records data. Through lectures, hands-on experiments, public electronic health records databases, basic machine learning algorithms, basic statistical skills and tools, class and small group discussions, and oral presentations, we will delve into the computational world with emphasis on human diseases like cardiovascular disease and health equity issues. The hands-on patient cohort selection and machine learning modeling will emphasize the proper use of the scientific method to answer a research question, develop a hypothesis, carry out computational tasks, make observations, analyze, interpret and communicate results.

Learning Units:

• Introduction to the scientific method of research; with emphasis on hypothesis generation and testing, using statistical tests.
• Introduction to statistical sampling and patient selection criteria.
• Introduction to social determinants of health (SDoH) and their importance in patient care and health outcomes.
• Introduction to machine learning modeling tools to detect and “explain” health equity gaps and their association with social determinants of health.

Download PDF