Machine Learning Projects in Healthcare

Solve real-world healthcare challenges using machine learning. Modeled after the popular BIOMEDIN215 Stanford graduate course, this professional course explores the unique data challenges of the healthcare industry and how machine learning can be applied to help solve them. In this course, we introduce methods for using large-scale electronic medical records data for machine learning, applying text mining to medical records, and for using ontologies for the annotation and indexing of unstructured content as well as for intelligent feature engineering.

Throughout the course, you will work through interactive exercises and case studies, attend live webinars from Stanford faculty and guest speakers, receive ongoing feedback from our course team, and collaborate with your fellow learners. Gain the real-world skills you need to run your own machine learning projects in industry.

• Work with healthcare data and how to use data to conduct research studies
• Differentiate between categories of research questions and the study designs used to address them
• Describe common healthcare data sources and their advantages and limitations relative to different research questions
• Extract and transform various kinds of clinical data to create analysis-ready datasets
• Execute tasks involving data manipulation and analysis