Episode
ML Foundations and Methods for Precision Medicine and Healthcare
with Suchi Saria, Peter Schulam
Electronic health records and high throughput measurement technologies are changing the practice of healthcare to become more algorithmic and data-driven. This offers an exciting opportunity for machine learning to impact healthcare.
The aim of this tutorial is to introduce you to the most important challenges and techniques for developing "personalized decision-making" tools in medicine. We will also cover example data sources and describe ongoing national initiatives that provide a way for you to get involved.
Target audience: The majority of this tutorial will be targeted at an audience with basic machine learning knowledge. No background in medicine or health care is needed. We will make our slides and any relevant documents accessible after the talk.
Learning objectives: - Become familiar with important (computational) problems in precision medicine and individualized health care - Get introduced to state-of-the-art approaches - Hear about relevant datasets (and potential funding sources).
Electronic health records and high throughput measurement technologies are changing the practice of healthcare to become more algorithmic and data-driven. This offers an exciting opportunity for machine learning to impact healthcare.
The aim of this tutorial is to introduce you to the most important challenges and techniques for developing "personalized decision-making" tools in medicine. We will also cover example data sources and describe ongoing national initiatives that provide a way for you to get involved.
Target audience: The majority of this tutorial will be targeted at an audience with basic machine learning knowledge. No background in medicine or health care is needed. We will make our slides and any relevant documents accessible after the talk.
Learning objectives: - Become familiar with important (computational) problems in precision medicine and individualized health care - Get introduced to state-of-the-art approaches - Hear about relevant datasets (and potential funding sources).
Have feedback? Submit an issue here.