• Examines the practical considerations for operationalizing machine learning in healthcare settings, with a focus on robust, private, and fair modeling using real retrospective healthcare data. Explores the pre-modeling creation of dataset pipeline to the post-modeling"implementation science," which addresses how models are incorporated at the point of care. Students complete three homework assignments (one each in machine learning, visualization, and implementation), followed by a project proposal and presentation. Students gain experience in dataset creation and curation, machine learning training, visualization, and deployment considerations that target utility and clinical value. Students partner with computer scientists, engineers, social scientists, and clinicians to better appreciate the multidisciplinary nature of data science.

• Textbook Information

There is no information available for this course.