10-742 Machine Learning in Healthcare
12 units, Carnegie Mellon University
Healthcare is not only the largest sector in the US economy, accounting for 20 of the GDP, but it also has a profound impact on our lives. Machine learning ML is experiencing explosive growth in healthcare, and is now top of mind for leaders at hospitals, insurance companies, and pharmaceutical firms. This course offers a survey of ML in healthcare today. The course will cover how ML is impacting care delivery, in particular in radiology, pathology, and ophthalmology. Students will learn how to apply causal inference, anomaly detection, Bayesian statistics, natural language processing, and large language models to important problems in healthcare, such as diagnosing a patient with a complex set of conditions, and predicting how long a patient will remain in the hospital. Students will gain firsthand experience extracting knowledge from electronic health records, timeseries medical data, and other healthcare data sources. As a recurring theme, the course will cover the many challenges of working responsibly with healthcare data, including potential biases and inconsistencies, and provide strategies for identifying and mitigating these issues.
Prerequisites: 10-301 (min C) or 10-315 (min C) or 10-715 (min C) or 10-701 (min C) or 10-601 (min C)
Courseweave needs JavaScript to show sections, workloads and planning tools.