Clinical Data Learning, Visualization, and Deployments

HST.953 is a course about the practical considerations for operationalizing machine learning in healthcare settings. We begin the course with a focus on robust, private and fair machine learning (ML) using real retrospective healthcare data. We follow this with experiences in visualization (VIS) that target utility and clinical value. Finally, we explore the intermediate “implementation science” (IMP) tying together how real models might be potentially used through a visual system by practicing clinical staff. **ACKNOWLEDGEMENTS** Rodrigo Gameiro assisted with organization of course materials for publication on MIT OpenCourseWare.

This course provides an introductory survey of data science tools in healthcare. It was created by members of {{% resource_link "491927c1-9577-4956-8065-808d678be313" "MIT Critical Data" %}}, a global consortium consisting of healthcare practitioners, computer scientists, and engineers from academia, industry, and government, that seeks to place data and research at the front and center of healthcare operations. The most daunting global health issues right now are the result of interconnected crises. In this course, we highlight the importance of a multidisciplinary approach to health data science. It is intended for front-line clinicians and public health practitioners, as well as computer scientists, engineers, and social scientists, whose goal is to understand health and disease better using digital data captured in the process of care. What you'll learn: * Principles of data science as applied to health * Analysis of electronic health records * Artificial intelligence and machine learning in healthcare This course is part of the {{% resource_link "756906cb-f32e-45fb-8c91-b34efa30463c" "Open Learning Library" %}}, which is free to use. You have the option to sign up and enroll in the course if you want to track your progress, or you can view and use all the materials without enrolling.