Machine Learning for Healthcare from Massachusetts Institute of Technology
Machine learning methods have revolutionized many aspects of healthcare, from new models that help clinicians make more informed decisions to new technologies that enable individual patients to better manage their own health. Since the 1950s with Kaiser’s first computerized records for chest X-ray reports and blood test results, and the introduction of the pacemaker, clinicians have realized the potential of algorithms to save lives. This rich history of machine learning for healthcare informs groundbreaking research today, as new advances in image processing, deep learning, and natural language processing are transforming the healthcare industry.
The course begins with an introduction to clinical care and data, and then explores the use of machine learning for risk stratification and diagnosis, disease progression modeling, improving clinical workflows, and precision medicine. For each of these topics, we dive into methodological details typically not covered in introductory machine learning courses, such as the foundations of deep learning on imaging and natural language, interpretability of ML models, algorithmic fairness, causal inference, and off-policy reinforcement learning.
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