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HST.953 | Fall 2024 | Graduate

# Clinical Data Learning, Visualization, and Deployments

#### Course Description

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 …

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 organizataion of course materials for publication on MIT OpenCourseWare.

## Course Info

##### Instructors

- Marzyeh Ghassemi
- Leo A Celi
- Adam Rodman
- Ned McCague

##### Departments

- Electrical Engineering and Computer Science
- Health Sciences and Technology

##### Topics

- Engineering
  - Computer Science
    - Artificial Intelligence
- Health and Medicine

##### Learning Resource Types

assignment Problem Sets
assignment Programming Assignments
auto_stories Readings
The diagram above outlines the pattern recognition system that is used for automatic decision-making in machine learning. Figure 1 from Andreas Maier, et al. “A gentle introduction to deep learning in medical image processing.” Zeitschrift für Medizinische Physik 29 no. 2 (2019): 86–101. License: CC BY 4.0. (Extra white-space added above and below original image.)

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