HST.956
Machine Learning for Healthcare
See description under subject 6.7930J.
Spring 2019
Topics
- Introduction: What Makes Healthcare Unique?
- Overview of Clinical Care
- Deep Dive into Clinical Data
- Risk Stratification, Part 2
- Physiological Time-Series
- Natural Language Processing (NLP), Part 2
- Machine Learning for Differential Diagnosis
- Causal Inference, Part 1
- Causal Inference, Part 2
- Disease Progression & Subtyping, Part 1
- Disease Progression & Subtyping, Part 2
- Precision Medicine
- Automating Clinical Workflows
- Fairness
- Robustness to Dataset Shift
- Interpretability
Readings
- Ses #: 2 · Required Readings: No required readings. · Optional Readings: No readings.
- Ses #: 8 · Required Readings: No required readings.
- Ses #: 9 · Required Readings: No required readings.
- Ses #: 10 · Required Readings: Ranschaert, Erik, Sergey Morozov, and Paul Algra. “Chapter 13: Artificial Intelligence and Computer-Assisted Evaluation of Chest Pathology.” In Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks . Cham, Switzerland: Springer, 2019. ISBN: 9783319948775.
- Ses #: 15 · Required Readings: No required readings.
- Chakraborty, Bibhas, and Erica Moodie. Statistical Methods for Dynamic Treatment Regimes. Section 2.1, 2.2, and Chapter 3. Springer, 2013. ISBN: 9781461474272.
- Ses #: 21 · Required Readings: No required readings.
- Ses #: 23–25 · Required Readings: No required readings. · Optional Readings: No readings.
Prerequisites & course information
Same subject as 6.7930J Prereq: 6.3900 , 6.7810 , 6.7900 , 6.8611 , or 9.520[J] G (Spring) Not offered regularly; consult department 4-0-8 units
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Spring 2019
- MIT OCW calendar ↗
- MIT OCW readings ↗
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.