Clinical Data Learning, Visualization, and Deployments
MIT's HST.953 covers the practical work of operationalizing machine learning in healthcare settings. The course opens with robust, private, and fair machine learning methods applied to real retrospective healthcare data, then moves into visualization techniques aimed at clinical utility and value. A final module on implementation science ties the two together, examining how predictive models might actually be used through visual systems by practicing clinical staff. Materials come from MIT OpenCourseWare and include course readings and structured modules covering ML fairness, privacy, and visualization design for clinical audiences. The course is aimed at an advanced audience already familiar with machine learning basics, and it emphasizes the gap between building a model and deploying one that clinicians will trust and use. No cost is required to access the materials.